Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms
Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention stra...
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| Published in | BMC bioinformatics Vol. 19; no. Suppl 9; pp. 283 - 121 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
13.08.2018
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/s12859-018-2277-0 |
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| Abstract | Background
The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.
Results
Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy.
Conclusions
Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. |
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| AbstractList | Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. Results Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. Conclusions Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. Keywords: Diabetic retinopathy, Clinical decision support, Machine learning, Risk factors The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. BackgroundThe risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.ResultsExperimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy.ConclusionsOur method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.BACKGROUNDThe risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions.Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy.RESULTSExperimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy.Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future.CONCLUSIONSOur method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. Results Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. Conclusions Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. Abstract Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. Results Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. Conclusions Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future. |
| ArticleNumber | 283 |
| Audience | Academic |
| Author | Tsao, Hsin-Yi Chan, Pei-Ying Su, Emily Chia-Yu |
| Author_xml | – sequence: 1 givenname: Hsin-Yi surname: Tsao fullname: Tsao, Hsin-Yi organization: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Division of Endocrinology and Metabolism, Department of Internal Medicine, Sijhih Cathay General Hospital – sequence: 2 givenname: Pei-Ying surname: Chan fullname: Chan, Pei-Ying organization: Department of Occupational Therapy and Healthy Aging Center, Chang Gung University, Department of Psychiatry, Linkou Chang Gung Memorial Hospital – sequence: 3 givenname: Emily Chia-Yu surname: Su fullname: Su, Emily Chia-Yu email: emilysu@tmu.edu.tw organization: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Clinical Big Data Research Center, Taipei Medical University Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30367589$$D View this record in MEDLINE/PubMed |
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| Issue | Suppl 9 |
| Keywords | Diabetic retinopathy Clinical decision support Risk factors Machine learning |
| Language | English |
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| References | Q Gong (2277_CR16) 2011; 54 EY Chew (2277_CR1) 1996; 114 Early Treatment Diabetic Retinopathy Study Research Group (2277_CR6) 1995; 113 B Kowall (2277_CR10) 2013; 6 E Oh (2277_CR14) 2013; 13 T Aspelund (2277_CR12) 2011; 54 2277_CR18 UK Pospective Diabetes Study Group (2277_CR9) 1998; 317 2277_CR15 JC Lin (2277_CR17) 2014; 132 JH Kempen (2277_CR4) 2004; 122 YY Huang (2277_CR5) 2012; 111 SM Hosseini (2277_CR11) 2009; 14 Tien-Jyun Chang (2277_CR7) 2012; 111 American Diabetes Association (2277_CR2) 2014; 37 F Semeraro (2277_CR13) 2011; 25 DS Fong (2277_CR3) 2004; 27 JW Yau (2277_CR8) 2012; 35 |
| References_xml | – volume: 54 start-page: 2525 issue: 10 year: 2011 ident: 2277_CR12 publication-title: Diabetologia doi: 10.1007/s00125-011-2257-7 – volume: 6 start-page: 477 year: 2013 ident: 2277_CR10 publication-title: Diabetes Metab Syndr Obes doi: 10.2147/DMSO.S39093 – volume: 111 start-page: 637 issue: 11 year: 2012 ident: 2277_CR5 publication-title: J Formos Med Assoc doi: 10.1016/j.jfma.2012.09.006 – ident: 2277_CR15 – volume: 122 start-page: 552 issue: 4 year: 2004 ident: 2277_CR4 publication-title: Arch Ophthalmol doi: 10.1001/archopht.122.4.552 – volume: 25 start-page: 292 issue: 5 year: 2011 ident: 2277_CR13 publication-title: J Diabetes Complicat doi: 10.1016/j.jdiacomp.2010.12.002 – volume: 113 start-page: 1144 issue: 9 year: 1995 ident: 2277_CR6 publication-title: Arch Ophthalmol doi: 10.1001/archopht.1995.01100090070025 – volume: 13 start-page: 106 issue: 1 year: 2013 ident: 2277_CR14 publication-title: BMC Med Inform Decis Mak doi: 10.1186/1472-6947-13-106 – volume: 132 start-page: 922 issue: 8 year: 2014 ident: 2277_CR17 publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2014.859 – volume: 111 start-page: 605 issue: 11 year: 2012 ident: 2277_CR7 publication-title: Journal of the Formosan Medical Association doi: 10.1016/j.jfma.2012.09.011 – volume: 14 start-page: 105 issue: 2 year: 2009 ident: 2277_CR11 publication-title: J Res Med Sci – volume: 35 start-page: 556 issue: 3 year: 2012 ident: 2277_CR8 publication-title: Diabetes Care doi: 10.2337/dc11-1909 – volume: 37 start-page: S14 issue: Suppl 1 year: 2014 ident: 2277_CR2 publication-title: Diabetes Care doi: 10.2337/dc14-S014 – volume: 27 start-page: S84 issue: Suppl 1 year: 2004 ident: 2277_CR3 publication-title: Diabetes Care doi: 10.2337/diacare.27.2007.S84 – ident: 2277_CR18 doi: 10.1186/s12859-018-2277-0 – volume: 317 start-page: 703 issue: 7160 year: 1998 ident: 2277_CR9 publication-title: BMJ doi: 10.1136/bmj.317.7160.703 – volume: 54 start-page: 300 issue: 2 year: 2011 ident: 2277_CR16 publication-title: Diabetologia doi: 10.1007/s00125-010-1948-9 – volume: 114 start-page: 1079 issue: 9 year: 1996 ident: 2277_CR1 publication-title: Arch Ophthalmol doi: 10.1001/archopht.1996.01100140281004 |
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The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more... The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated... Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more... BackgroundThe risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more... Abstract Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors... |
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| SubjectTerms | Adult Aged Aged, 80 and over Algorithms Analysis Artificial intelligence Artificial neural networks Bioinformatics Biomedical and Life Sciences Blood pressure Clinical decision support Clinical medicine Computational biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data mining Datasets Decision Support Systems, Clinical Decision trees Diabetes Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - complications Diabetic retinopathy Diabetic Retinopathy - diagnosis Diabetic Retinopathy - etiology Diagnosis Edema Female Hemorrhage Humans Insulin Learning algorithms Life Sciences Machine Learning Male Methods Microarrays Middle Aged Neural networks Neural Networks (Computer) Patients Performance prediction Prediction models Retina Retinopathy Risk analysis Risk Factors ROC Curve Studies Support systems Support vector machines Test procedures |
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| Title | Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms |
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