A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence.
Diabetic retinopathy (DR) is vision impairment and a life-threatening condition for diabetic patients. Especially type II diabetic people have higher chances of getting retinal problems. Hence, early prediction of DR is necessary for preventing the diabetic patients from vision impairment. The main...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 22 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
8/8/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=170716971&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 170716971 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/8/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 170716971 170716971 170716971 10.1007/s10916-023-01976-7 170716971 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence. aug: au: Lalithadevi, B. Krishnaveni, S. Gnanadurai, J. Samuel Cornelius affil: Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India sug: subj: Diabetic Retinopathy Diagnosis Diabetes Mellitus, Type 2 Artificial Intelligence Early Diagnosis Diabetic Retinopathy Risk Factors Risk Assessment Prediction Models Human Diabetic Patients Analytic Research Questionnaires Health Screening Random Sample Adult Middle Age Aged Severity of Illness Blood Glucose Machine Learning Descriptive Statistics Inferential Statistics Algorithms Ophthalmologists Male Female Analysis of Variance Data Analysis Software Regression Aged, 80 and Over Body Mass Index Factor Analysis Chi Square Test Behavioral and Mental Disorders Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Diabetic retinopathy (DR) is vision impairment and a life-threatening condition for diabetic patients. Especially type II diabetic people have higher chances of getting retinal problems. Hence, early prediction of DR is necessary for preventing the diabetic patients from vision impairment. The main aim of this feasibility study is to identify the most critical risk features that could lead to diabetic retinopathy. This study investigated type II diabetic patients' socio-analytical, diabetes, behavioral, and clinical risk factors. We conducted a self-individual questionnaire session for all participants. Our questionnaire asked about the reliability of results, feeling comfortable during the screening test, willingness to participate in future screenings, overall perspective, and satisfaction with the DR screening test. We proposed a random forest model for predicting the prevalence of DR risk among diabetics. Further explanations of the model were conducted using more robust SHAP eXplainable Artificial Intelligence (XAI) tools. The SHAP method makes it possible to understand how input variables interact with their representative output records, as well as how input variables are ranked. In addition, various descriptive and inferential statistical analyses were performed on the data and evaluated the significant relationship between the factors discussed above via hypothesis testing. This feasibility study involved 172 type II diabetic patients (73 males and 99 females). Therefore, we found that 81 (47.09%) out of 172 participants had referable DR. The average age of the patients was determined as 55.08, with a standard deviation of ± 9.770 (ranging from 40 to 79). Type II patients were affected by mild, moderate, severe, and advanced proliferative diabetic retinopathy (PDR) stages with 23.83%, 13.95%, 5.81%, and 3.48%, respectively, of the total samples. The developed RF model obtained high accuracy of 94.9% using clinical dataset. Our results showed that the formation of tiny microminiature lesions was noticeable in type II diabetic patients with aged people, abnormal blood glucose levels, and prolonged diabetes duration. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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