Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

  • Ramon Casanova ,

    Affiliation Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Santiago Saldana,

    Affiliation Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Emily Y. Chew,

    Affiliation National Eye Institute, National Institutes of Health [NIH], Bethesda, Maryland, United States of America

  • Ronald P. Danis,

    Affiliation Fundus Photograph Reading Center, University of Wisconsin, Madison, Wisconsin, United States of America

  • Craig M. Greven,

    Affiliation Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

  • Walter T. Ambrosius

    Affiliation Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America

Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

  • Ramon Casanova, 
  • Santiago Saldana, 
  • Emily Y. Chew, 
  • Ronald P. Danis, 
  • Craig M. Greven, 
  • Walter T. Ambrosius



Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects.


Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance.

Principal Findings

Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables.

Conclusions and Significance

We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.


Diabetes results from the insufficient generation of insulin by the pancreas, or deficient insulin processing in the body. In 2011, the National Institutes of Health estimated there were 25.8 million people affected by diabetes in the US (8.3% of the population). Diabetic retinopathy (DR) is a common complication of diabetes; it affects more than 4.4 million people in the US aged 40 and older, and is one of the leading causes of blindness in the nation. Among those with diabetes, it is estimated that worldwide, approximately 93 million people may have some DR, and 28 million may have sight-threatening stages of DR [1]. Some of the major DR risks are considered to be duration of diabetes, blood pressure, glycemic control [2], [3], dyslipidemia [4], and nephropathy. Since not all diabetic patients develop DR some researchers believe genetic factors are involved [5].

DR is a silent disease that may not be detected until it is too late for effective treatment; therefore early detection could improve the chances of therapeutic interventions to alleviate its effects. Currently, DR detection is based on clinical examination or evaluation of digital color fundus photographs of the retina. These photographs are examined for evidence of lesions associated with DR, such as microaneurysms, hemorrhages, neovascularization or other vascular abnormalities and hard exudate deposits. Although this approach works well in general, the expertise needed to detect these problems is uncommon and intra- and inter-observer variability can affect the quality of the process [6]. Lack of expertise and equipment to diagnose DR is common, especially in rural areas or less developed countries. This has motivated increasing efforts to develop automated methods for DR detection using image processing, pattern recognition, and machine learning methods [6][8]. Most efforts have focused on creating automated systems that use the fundus photography images as input [9][12]. While good progress has been made and automated systems are beginning to reach standards similar to those of clinicians, the examination of fundus photography by experts still remains the gold standard.

Most previous machine learning research for potential use in DR is based on support vector machines (SVM) or other methods. Here we introduce Random Forests (RF) to DR classification analyses based on fundus photography data. RF is a powerful machine learning method for classification and regression which compares well with other state-of-the-art classifiers such as SVM [13] and ADABOOST [14]. The strengths of the RF approach are that: 1) it does not overfit; 2) it is robust to noise; 3) it has an internal mechanism to estimate error rates, called out-of-the-bag (OOB) error; 4) it provides indices of variable importance; 5) it naturally works with mixes of continuous and categorical variables; and 6) it can be used for data imputation and cluster analysis. These properties have made RF increasingly popular in the last few years, especially in the field of genetics and imaging [15][19]. In addition, rather than focusing on discriminating patients with DR from controls we proposed metrics for DR risk assessment based on Random Forests methods [20] using existing graded fundus photography and systemic data. The graded data from fundus photography can potentially contain subtle multivariate patterns predictive of early DR undetected by human experts. In this situation, the ability of high-dimensional machine learning algorithms to deal with multiple variables could be of great benefit.

The Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was designed to evaluate the effects of intensive versus standard interventions to control glucose, systolic blood pressure, and lipid levels on incidence of serious cardiovascular events in people with type 2 diabetes mellitus [21][23]. ACCORD-Eye was a substudy that sought to assess the effects of the interventions on retinal pathology at baseline and after 4 years in a subset of ACCORD participants [24]. The prospective study design, large sample size, baseline and follow-up fundus photography data, and the systematic record of eye events in ACCORD-Eye provided an opportunity to develop methods for early DR prediction.

We take advantage of our access to a well-characterized clinical database such as ACCORD-Eye to introduce RF to classification analyses of DR. We evaluated (a) its performance relative to logistic regression, a more conventional statistical approach, and (b) the impact of sample sizes on both classifiers. Finally, we have recently proposed the class-conditional probabilities generated by high-dimensional classifiers as a measure of risk for progression to Alzheimer’s disease (AD) [25][29]. Here we evaluate the use of class-conditional probabilities produced by RF to assess risk of future DR events in ACCORD-Eye participants. Our DR risk assessment metrics were derived from the fundus photography grading and systemic data obtained in the ACCORD study. These results could be a useful contribution for early detection of DR, and provide an interesting application for a highly valuable fundus photography database from over 3,400 individuals with diabetes mellitus.

Materials and Methods

ACCORD-eye Study

The design of the ACCORD-Eye study has been previously reported [24]. Briefly, in ACCORD, 10,251 middle-aged and elderly people with type 2 diabetes, hemoglobin A1C levels ≥7.5%, and additional cardiovascular disease risk factors were randomized to a glucose-lowering trial and either a blood pressure-lowering or fibrate trial. Cardiovascular events were ascertained every 4 months. ACCORD participants without a history of proliferative diabetic retinopathy treated with laser photocoagulation or vitrectomy also were eligible for the ACCORD Eye study. All ACCORD-Eye study participants provided written informed consent both for the overall ACCORD trial and the substudy. The ACCORD trial’s primary outcome was a composite comprising the first occurrence of a nonfatal myocardial infarction (MI), nonfatal stroke, or cardiovascular death. Secondary outcomes analyzed included total MIs and total strokes (i.e. fatal or nonfatal), cardiovascular death, and death from any cause. These study outcomes were adjudicated by investigators masked to treatment allocation. The mean follow-up period for the primary outcome and mortality were 4.7 years and 5.0 years, respectively.

The eye assessment consisted of comprehensive standardized eye examinations by a study ophthalmologist or optometrist, and fundus photography comprising seven standard stereoscopic fields obtained at baseline from 3433 subjects and at 4 years of follow-up available for 2856 participants. The fundus photographs were centrally graded by individuals masked to treatment allocation according to a modified version of the Early Treatment Diabetic Retinopathy Study (ETDRS) [30]. The severity of retinopathy at baseline and follow-up was classified as either no retinopathy; mild nonproliferative diabetic retinopathy (NPDR); moderate NPDR; or severe retinopathy (i.e. severe NPDR, proliferative retinopathy, or incident laser therapy or vitrectomy since baseline). Deterioration in diabetic retinopathy was classified as a <2-step, 2- to 3-step, or 3-step change using the steps in the ETDRS person scale that evaluated both eyes. Anyone who had laser therapy or vitrectomy was deemed to have developed the most severe stage of diabetic retinopathy and was grouped with the >3-step change category. In this work, we will refer to changes from baseline leading to the 3-step change category as DR events. Information about the number of participants and DR events during follow-up in each DR severity group and follow-up diagnosis for the healthy participants at baseline is provided in Tables 12.

Table 1. Baseline stratification of subjects across DR severity groups and numbers of eye events per group is provided.

Table 2. Diagnosis after four years of follow-up for subjects without DR at baseline, and eye events for each subgroup.

Random Forests

RF is one of the so-called ensemble methods for classification, because a committee of learners (trees in this case) is generated and each one casts a vote for the predicted label of a given instance. The trees are built using the classification and regression trees methodology (CART) [31]. In constructing the ensemble of trees, RF uses two types of randomness: first, each tree is grown using a bootstrapped version of the training data. A second level of randomness is added when growing the tree by selecting a random sample of predictors at each node to choose the best split. The number of predictors selected at each node and the number of trees in the ensemble are the two main parameters of the RF algorithm. The RF developers have reported [20] that the method does not require much tuning of the parameters and the default values often produce good results for many problems. Once the forest is built, assigning a new instance to a class is accomplished by combining the trees, using a majority vote. As a result of using a bootstrap sampling of the training data, around one-third of the samples are omitted when building each tree. These are the so-called out-of-the-bag (OOB) samples that can be used to assess the performance of the classifier and to build measures of importance.

In this work, we used the permutation importance index to assess variable importance. The importance of a variable is evaluated by estimating the change in prediction error occurring when the variable in the OOB data is randomly permuted while others are left unchanged. The calculations are carried out tree by tree as the random forest is constructed. If a variable is important in a problem under analysis, permuting its values at random leads to larger changes in prediction performance compared to those that are unimportant. We used here the randomForest package in R [32] and its default parameters for RF: number of trees (ntree) equal to 500 and number of variables analyzed at each node to find the best split G where is the total number of variables in the problem.

Finally, we used RF capabilities for data imputation, which is based on the concept of proximities in RF. Proximities form a square matrix () and in some sense are a measure of distance between two samples. To compute proximities after a tree is grown, all of the data, both training and OOB, are put down the tree. If two observations are in the same terminal node, their proximity is increased by one. At the end, the proximities are normalized by dividing by the number of trees. The proximity matrix is used to update the imputation of the missing values. For continuous predictors, the imputed value is the weighted average of the non-missing observations, where the weights are the proximities. For categorical predictors, the imputed value is the category with the largest average proximity. This process is iterated several times.


As variables we used measurements derived from fundus photography and systemic baseline data (see Tables S1 and S2) from the 3,433 ACCORD Eye participants. Follow-up data were available for 2856 participants. The data were formatted for analyses using the randomForest R library and the software package Weka [33]. Missing data were imputed using RF imputation methods [34]. Fields providing explicit information about DR severity were removed from the graded data.

For classification analyses we collapsed mild, moderate and severe DR groups into one class of subjects having DR which produced two classes: 1) those with no DR and 2) those with DR according to the graders assessments. Using baseline ACCORD-Eye data, we evaluated the accuracy of different classification models generated by combining (concatenating) fundus photography grading data with systemic variables. Specifically, we studied the value of the grading data (Eye data) and systemic data independently and combined for discriminating participants with and without DR, using both RF and the standard logistic regression. We selected logistic regression because: a) it is a conventional statistical method that models conditional probabilities, and b) it can highlight differences between RF and a traditional statistical method. The RF permutation index of variable importance was used to determine which variables play a major role when discriminating participants with and without DR. In addition, we studied the impact of the sample size on both algorithms’ performance. Sample sizes varied from 50 to 1700. In each case, 100 data training and testing datasets of the same size were drawn at random from the entire dataset. The models were estimated using the training datasets, while classification accuracy was estimated using the testing datasets, which is equivalent to a two-fold cross-validation. To evaluate the quality of the OOB RF mechanism, we calculated OOB accuracy rates to compare them with the results of the two-fold cross-validation. We also asked an expert to select a subset of eye variables with more clinical relevance (see Table S3). The analyses were rerun to study the impact of this selection on the performance of both methods.

Finally, we computed a RF model using data from all participants. The class-conditional probabilities of having DR were estimated at baseline for all participants who were diagnosed as not having DR and had follow up data. We then compared the probabilities of the subjects who later had eye events with the probabilities of those who did not, using the Wilcoxon rank sum test. Statistical testing was performed in SAS. The analyses were performed using probabilities computed for three different scenarios: 1) RF based on all variables; 2) RF based on ACCORD-Eye data only; and 3) RF based on systemic data only.


Figure 1 shows the RF classification accuracy estimated using the OOB mechanism built into RF, compared with the classification accuracy estimated using the testing datasets. Our results show that the RF OOB mechanism produces accurate estimates of RF performance within the evaluated sample sizes, suggesting reliable estimates of classifier performance for the full sample size in this study. RF outperformed LR in terms of classification accuracy across the three situations we studied (Figure 2). This advantage is very likely explained by RF nonlinearity and its capability to detect important variables in the model while discarding the effects of the non-relevant ones. Selecting a subset of clinical relevant variables in the graded fundus photography data based on an expert’s assessment led to moderate improvements in classification performance for logistic regression but had a much smaller impact on RF performance (see Figure 3). This experiment highlights RF robustness to the presence of large amount of non-relevant variables in the model.

Figure 1. Estimates of RF classification accuracy obtained using the OOB mechanism and two-fold CV.

RF models were estimated using all the available variables.

Figure 2. Performance across sample sizes of both RF (right panel) and LR is shown for three different scenarios: 1) Only eye data; 2) all variables in the study; and 3) only systemic data.

The addition of systemic variables did not lead to significant increases in classification accuracy.

Figure 3. RF and LR performance using all available eye variables and a subset of eye variables selected by an expert as more clinically relevant.

While this selection led to some improvements for LR it had very little impact on RF performance.

Studies that report performance of classifiers’ performance across sample sizes are uncommon. We took advantage here of the size of the ACCORD-Eye dataset to evaluate the behavior of RF across sample sizes. Our experiments with fundus photography data provide empirical support for some reported RF properties such as the excellent quality of the estimates of accuracy provided by the OOB RF mechanism and its capability to discard the effect of non-relevant variables. The combination of graded fundus photography data with systemic variables in the model did not increase prediction performance, although systemic variables were informative regarding disease status (accuracy >75%). Finally, RF performance tended to stabilize at 500 samples. Further sample sizes increases produced very little or no gain in performance. On the other hand, LR-associated accuracies seemed to increase as the sample size increased.

This work provides insight into which variable might be most relevant to diagnose DR. The five most important variables for each scenario are presented in Table 3. RF measure of variable importance permutation index was used to rank the variables. Microaneurysm counts in both eyes were detected by RF as the most important variable for classification, both in the eye data only and in the combined data analyses. Number of medicines and diabetes duration also were important role in the analyses. Table 4 presents the average probabilities of having DR for two groups of participants who were not diagnosed with DR at baseline (a) participants who had a DR event (3 step ETDRS progression, vitrectomy, or laser photocoagulation) during the 4 years of follow up, and (b) participants who did not have a DR event. Participants who had the DR events were at a significantly higher risk of DR at baseline relative to those who did not have the DR events during follow-up. Although adding systemic data did not increase classification accuracy, combining both types of data lead to increased statistical discrimination of DR risk between those who did not have DR events during follow up and those who did.

Table 3. Most relevant variables according to RF permutation index criterion for each type of data.

Table 4. The RF probabilities of having DR were estimated for two groups of participants who were not diagnosed as DR at baseline: a) those who had a DR event (> = 3 step ETDRS progression, vitrectomy, or laser photocoagulation) during follow-up and 2) those who did not.


The main contribution of this work is the introduction of Random Forests methods to DR data analyses. Most previous work is based on the use of support vector machines (SVM) or other pattern recognition techniques. RF has shown great potential for DR classification and detection of relevant features in the fundus photography data. RF is highly nonlinear and works well with high-dimensional data clearly outperforming classic logistic regression. Another contribution is that while previous machine learning-based DR research has focused on classification of fundus photography images from patients with or without DR, here we have addressed the development of metrics to identify subjects at risk of DR in the very early stages. Our results suggest that it is possible to devise metrics that identify groups of participants at higher risk of DR. In the DR literature, only Rajendra and colleagues (using a very different rationale) have proposed an integrated index for DR identification based on images texture parameters [35]. Our work, on the other hand, is a translation of our Alzheimer’s disease (AD) research, where we have shown that similar metrics generated by classifiers estimated using neuroimaging and cognitive data are informative when discriminating between groups of subjects with mild cognitive impairment who will progress to AD from those who remain stable [28], [29], [36], [37]. Studies of the sample size effect on performance of machine learning algorithms are rare in the literature. Besides the mathematical interest of this question, this knowledge could save resources in clinical trials by reducing unnecessary computation when estimating prediction models.

Here, RF capabilities to detect relevant patterns in the data produced very meaningful results that correlate well with criteria for DR diagnosis and with known risk factors. For example, microaneurysms counts, microvascular abnormalities and hard exudates are important criteria used by clinicians to diagnose DR and evaluate its severity when using fundus photography data. Diabetes duration and blood pressure are widely recognized as major DR risk factors [1]. The number of medicines is an interesting finding. It could be an index of health status indicating that RF is detecting worsening in health associated with the DR group.

This work seeks to provide proof-of-concept for our methods in the area of DR prediction. However, our study does have some shortcomings. The main limitation is that our analyses were based on graded fundus photography data instead of the images themselves. To introduce into practice the approach proposed here, classification should be performed automatically using the fundus photographs replacing the grading by experts. We believe the quantification of subtle patterns in the images by pattern recognition and machine learning algorithms will help make these metrics much more accurate. Another possible source of improvement is using other types of information, such as genetic data, when available. Perhaps more sophisticated methods for data integration such as multiple kernel or manifold learning [38][40] will be more successful achieving synergy between different types of data than the concatenation of features we used. Another limitation is that our results can be confounded by misclassification errors by those who graded the images. This suggests an interesting application for our metrics that we will pursue in the future. Subjects declared as healthy by the graders but with high probability of having DR should raise a suspicion of misclassification error and those data should be reviewed by graders. This method could be a very useful tool for quality control in such large clinical trials as ACCORD-Eye. Finally, to have translational value, a validation study is needed using other databases.

We envision several very promising applications for the metrics proposed in this work that go beyond early detection. For example, they could be used to study DR genetics, which in general is poorly understood [41]. The genetic aspects of DR genetics are complex due to its relationship with glycemic control among other reasons. Our metrics could be used as quantitative traits in imaging genetic analyses of DR. We have recently reported that similar metrics derived from neuroimaging and cognitive data could increase statistical power in genome-wide association analyses of AD [27]. Recent research also provides growing evidence of association between small vessel disease and brain structure and function, suggesting the use of retinopathy as an earlier biomarker for brain small vessel disease [42]. A study published by the Women’s Health Initiative found associations between retinopathy, cognitive decline over 10 years, and ischemic lesion burden [43]. However, because retinopathy was relatively rare in that cohort, it was only evaluated as a dichotomized variable, limiting the ability to evaluate different degrees of retinopathy [42] – a situation where metrics such as those proposed here could be of great use. Finally, the metrics we have proposed here can be used to objectively select subjects at higher risk of DR for clinical trials if needed. This can spare resources by optimizing recruitment and decreasing required sample sizes.


In this work, we have introduced an approach based on Random Forest methods to perform classification analyses of DR fundus photography data. RF clearly outperforms logistic regression, a conventional statistical approach, in most of the situations we evaluated. In addition, we generated metrics that assess risk of diabetic retinopathy, using graded fundus photography and systemic data. These metrics are sensitive to patterns in the data associated with future DR events in subjects not presently affected by DR. We will work towards refining these metrics by using other types of information and more sophisticated machine learning methods for multimodal data analyses.

Supporting Information

Table S1.

List of variables available from the fundus photography grading.



Table S2.

List of systemic variables.



Table S3.

Subset of clinical relevant eye variables according to expert criterion.




The following companies provided study medications, equipment, or supplies: Abbott Laboratories, Amylin Pharmaceutical, AstraZeneca, Bayer HealthCare, Closer Healthcare, GlaxoSmithKline, King Pharmaceuticals, Merck, Novartis, Novo Nordisk, Omron Healthcare, Sanofi-Aventis, and Schering-Plough. We thank Karen Klein (Translational Science Institute, Wake ForestSchool of Medicine) for editing this manuscript.

Author Contributions

Conceived and designed the experiments: RC RD. Performed the experiments: SS RC. Analyzed the data: RC SS WA EC RD CG. Wrote the paper: RC SS WA EC RD CG.


  1. 1. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, et al. (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35: 556–564. doi: 10.2337/dc11-1909
  2. 2. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet 352: 837–853. doi: 10.1016/s0140-6736(98)07019-6
  3. 3. Progression of retinopathy with intensive versus conventional treatment in the Diabetes Control and Complications Trial. Diabetes Control and Complications Trial Research Group. Ophthalmology 102: 647–661. doi: 10.1016/s0161-6420(95)30973-6
  4. 4. van Leiden HA, Dekker JM, Moll AC, Nijpels G, Heine RJ, et al. (2002) Blood pressure, lipids, and obesity are associated with retinopathy: the hoorn study. Diabetes Care 25: 1320–1325. doi: 10.2337/diacare.25.8.1320
  5. 5. Esteves J, Laranjeira AF, Roggia MF, Dalpizol M, Scocco C, et al. (2008) [Diabetic retinopathy risk factors]. Arq Bras Endocrinol Metabol 52: 431–441. doi: 10.1590/s0004-27302008000300003
  6. 6. Abramoff MD, Niemeijer M, Russell SR (2010) Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev Med Devices 7: 287–296. doi: 10.1586/erd.09.76
  7. 7. Faust O, Acharya UR, Ng EY, Ng KH, Suri JS (2012) Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J Med Syst 36: 145–157. doi: 10.1007/s10916-010-9454-7
  8. 8. Priya R, Aruna P (2011) Review of automated diagnosis of diabetic retinopathy using support vector machines. International Journal of Applied Engineering Research 1: 844–862.
  9. 9. Abramoff MD, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russell SR, et al. (2008) Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31: 193–198. doi: 10.2337/dc07-1312
  10. 10. Quellec G, Lamard M, Abramoff MD, Decenciere E, Lay B, et al. (2012) A multiple-instance learning framework for diabetic retinopathy screening. Med Image Anal 16: 1228–1240. doi: 10.1016/
  11. 11. Quellec G, Lamard M, Cazuguel G, Bekri L, Daccache W, et al. (2011) Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs. Invest Ophthalmol Vis Sci 52: 8342–8348. doi: 10.1167/iovs.11-7418
  12. 12. Quellec G, Russell SR, Abramoff MD (2011) Optimal filter framework for automated, instantaneous detection of lesions in retinal images. IEEE Trans Med Imaging 30: 523–533. doi: 10.1109/tmi.2010.2089383
  13. 13. Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers; Pittsburgh. ACM. 144–152.
  14. 14. Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction: Springer.
  15. 15. Siroky DS (2008) Navigating Random Forests. Statistics Surveys.
  16. 16. Bureau A, Dupuis J, Falls K, Lunetta KL, Hayward B, et al. (2005) Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol 28: 171–182. doi: 10.1002/gepi.20041
  17. 17. Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P (2004) Screening large-scale association study data: exploiting interactions using random forests. BMC Genet 5: 32.
  18. 18. Casanova R, Whitlow CT, Wagner B, Espeland MA, Maldjian JA (2012) Combining graph and machine learning methods to analyze differences in functional connectivity across sex. Open Neuroimag J 6: 1–9. doi: 10.2174/1874440001206010001
  19. 19. Casanova R, Espeland MA, Goveas JS, Davatzikos C, Gaussoin SA, et al. (2011) Application of machine learning methods to describe the effects of conjugated equine estrogens therapy on region-specific brain volumes. Magn Reson Imaging 29: 546–553. doi: 10.1016/j.mri.2010.12.001
  20. 20. Breiman L (2001) Random Forests. Machine Learning 45: 5–32. doi: 10.1023/a:1010933404324
  21. 21. Group AS, Ginsberg HN, Elam MB, Lovato LC, Crouse JR 3rd, et al (2010) Effects of combination lipid therapy in type 2 diabetes mellitus. N Engl J Med 362: 1563–1574.
  22. 22. Group AS, Cushman WC, Evans GW, Byington RP, Goff DC Jr, et al (2010) Effects of intensive blood-pressure control in type 2 diabetes mellitus. N Engl J Med 362: 1575–1585.
  23. 23. Action to Control Cardiovascular Risk in Diabetes Study G, Gerstein HC, Miller ME, Byington RP, Goff DC Jr, et al. (2008) Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 358: 2545–2559. doi: 10.1056/nejmoa0802743
  24. 24. Chew EY, Ambrosius WT, Howard LT, Greven CM, Johnson S, et al. (2007) Rationale, design, and methods of the Action to Control Cardiovascular Risk in Diabetes Eye Study (ACCORD-EYE). Am J Cardiol 99: 103i–111i. doi: 10.1016/j.amjcard.2007.03.028
  25. 25. Casanova R, Whitlow CT, Wagner B, Williamson J, Shumaker SA, et al.. (2011) High dimensional classification of structural MRI Alzheimer’s disease data based on large scale regularization. Frontiers of Neuroscience in Neuroinformatics 5: 22. Epub 2011 Oct 14.
  26. 26. Casanova R, Maldjian JA, Espeland MA (2011) Evaluating the impact of different factors on voxel-wise classification methods of ADNI structural MRI brain images. International Journal of Biomedical Datamining 1: 11. doi: 10.4303/ijbdm/b110102
  27. 27. Casanova R, Chen S-H, Espeland MA, Hsu F-C (2012) Using Alzheimer’s disease probability scores as a quantitative trait in a genome wide association study; Irvine, California.
  28. 28. Casanova R, Hsu F-C, Sink KM, Rapp SR, Williamson J, et al.. (2013) Alzheimer’s disease risk assessment using large-scale machine learning methods; Seattle.
  29. 29. Casanova R, Hsu FC, Sink KM, Rapp SR, Williamson JD, et al. (2013) Alzheimer's disease risk assessment using large-scale machine learning methods. PLoS One 8: e77949. doi: 10.1371/journal.pone.0077949
  30. 30. Group AS, Group AES, Chew EY, Ambrosius WT, Davis MD, et al (2010) Effects of medical therapies on retinopathy progression in type 2 diabetes. N Engl J Med 363: 233–244.
  31. 31. Breiman L, Friedman JH, Olsen RA, Stone CJ (1984) Classification and Regression Trees: Chapman & Hall/CRC.
  32. 32. Liaw A, Wiener M (2002) Classification and Regression by randomForest. Rnews 2: 18–22.
  33. 33. Witten IH, Frank E (2005) Data Mining: Practical Machine Learning Tools and Techniques: ELSEVIER.
  34. 34. Pantanowics A, Marwala T (2009) Missing Data Imputation Through the Use of the Random Forest Algorithm. Advances in Intelligent and Soft Computing 116: 53–62. doi: 10.1007/978-3-642-03156-4_6
  35. 35. Acharya UR, Ng EY, Tan JH, Sree SV, Ng KH (2012) An integrated index for the identification of diabetic retinopathy stages using texture parameters. J Med Syst 36: 2011–2020. doi: 10.1007/s10916-011-9663-8
  36. 36. Casanova R, Hsu F-C, Chen S-H, Sink KM, Rapp SR, et al.. (2013) Assessment of Alzheimer’s disease risk using neuroimaging and cognitive data; Irvine, California.
  37. 37. Casanova R, Hsu F-C, Sink KM, Rapp SR, Williamson J, et al.. (2013) Alzheimer’s disease risk assessment using large-scale machine learning methods. PLos One (in Press).
  38. 38. Lanckriet GR, De Bie T, Cristianini N, Jordan MI, Noble WS (2004) A statistical framework for genomic data fusion. Bioinformatics 20: 2626–2635. doi: 10.1093/bioinformatics/bth294
  39. 39. Lanckriet GR, Deng M, Cristianini N, Jordan MI, Noble WS (2004) Kernel-based data fusion and its application to protein function prediction in yeast. Pac Symp Biocomput: 300–311.
  40. 40. Rakotomamonjy A, Bach FR, Canu S, Grandavalet Y (2008) SimpleMKL. Journal of Machine Learning Research 9: 2491–2521.
  41. 41. Ng DP (2010) Human genetics of diabetic retinopathy: current perspectives. J Ophthalmol 2010.
  42. 42. Gottesman RF, Li G (2012) Is brain health in the eye of the beholder? Neurology 78: 936–937. doi: 10.1212/wnl.0b013e31824de2bc
  43. 43. Haan M, Espeland MA, Klein BE, Casanova R, Gaussoin SA, et al. (2012) Cognitive function and retinal and ischemic brain changes: the Women’s Health Initiative. Neurology 78: 942–949. doi: 10.1212/wnl.0b013e31824d9655