Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study
<i>Background and Objectives:</i> Diabetes has become a global epidemic, contributing to significant health challenges due to its complications. Among these, diabetes can affect sight through various mechanisms, emphasizing the importance of early identification and management of vision-...
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2024-12-01
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| author | Adriana Ivanescu Simona Popescu Adina Braha Bogdan Timar Teodora Sorescu Sandra Lazar Romulus Timar Laura Gaita |
| author_facet | Adriana Ivanescu Simona Popescu Adina Braha Bogdan Timar Teodora Sorescu Sandra Lazar Romulus Timar Laura Gaita |
| author_sort | Adriana Ivanescu |
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| description | <i>Background and Objectives:</i> Diabetes has become a global epidemic, contributing to significant health challenges due to its complications. Among these, diabetes can affect sight through various mechanisms, emphasizing the importance of early identification and management of vision-threatening conditions in diabetic patients. Changes in the crystalline lens caused by diabetes may lead to temporary and permanent visual impairment. Since individuals with diabetes are at an increased risk of developing cataracts, which significantly affects their quality of life, this study aims to identify the most common cataract subtypes in diabetic patients, highlighting the need for proactive screening and early intervention. <i>Materials and Methods:</i> This study included 201 participants with cataracts (47.6% women and 52.4% men), of whom 105 also had diabetes. With the use of machine learning, the patients were assessed and categorized as having one of the three main types of cataracts: cortical (CC), nuclear (NS), and posterior subcapsular (PSC). A Random Forest Classification algorithm was employed to predict the incidence of different associations of cataracts (1, 2, or 3 types). <i>Results:</i> Cataracts have been encountered more frequently and at a younger age in patients with diabetes. CC was significantly more frequent among patients with diabetes (<i>p</i> < 0.0001), while the NS and PSC were only marginally, without statistical significance. Machine learning could also contribute to an early diagnosis of cataracts, with the presence of diabetes, duration of diabetes, or diabetic polyneuropathy (PND) having the highest importance for a successful classification. <i>Conclusions:</i> These findings suggest that diabetes may impact the type of cataract that develops, with CC being notably more prevalent in diabetic patients. This has important implications for screening and management strategies for cataract formation in diabetic populations. |
| format | Article |
| id | doaj-art-5dd6620f7875401cae9f4d03593c893e |
| institution | Kabale University |
| issn | 1010-660X 1648-9144 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medicina |
| spelling | doaj-art-5dd6620f7875401cae9f4d03593c893e2025-01-24T13:40:18ZengMDPI AGMedicina1010-660X1648-91442024-12-016112910.3390/medicina61010029Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional StudyAdriana Ivanescu0Simona Popescu1Adina Braha2Bogdan Timar3Teodora Sorescu4Sandra Lazar5Romulus Timar6Laura Gaita7Doctoral School of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaSecond Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaSecond Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaSecond Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaSecond Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaDoctoral School of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaSecond Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, RomaniaSecond Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania<i>Background and Objectives:</i> Diabetes has become a global epidemic, contributing to significant health challenges due to its complications. Among these, diabetes can affect sight through various mechanisms, emphasizing the importance of early identification and management of vision-threatening conditions in diabetic patients. Changes in the crystalline lens caused by diabetes may lead to temporary and permanent visual impairment. Since individuals with diabetes are at an increased risk of developing cataracts, which significantly affects their quality of life, this study aims to identify the most common cataract subtypes in diabetic patients, highlighting the need for proactive screening and early intervention. <i>Materials and Methods:</i> This study included 201 participants with cataracts (47.6% women and 52.4% men), of whom 105 also had diabetes. With the use of machine learning, the patients were assessed and categorized as having one of the three main types of cataracts: cortical (CC), nuclear (NS), and posterior subcapsular (PSC). A Random Forest Classification algorithm was employed to predict the incidence of different associations of cataracts (1, 2, or 3 types). <i>Results:</i> Cataracts have been encountered more frequently and at a younger age in patients with diabetes. CC was significantly more frequent among patients with diabetes (<i>p</i> < 0.0001), while the NS and PSC were only marginally, without statistical significance. Machine learning could also contribute to an early diagnosis of cataracts, with the presence of diabetes, duration of diabetes, or diabetic polyneuropathy (PND) having the highest importance for a successful classification. <i>Conclusions:</i> These findings suggest that diabetes may impact the type of cataract that develops, with CC being notably more prevalent in diabetic patients. This has important implications for screening and management strategies for cataract formation in diabetic populations.https://www.mdpi.com/1648-9144/61/1/29diabetescataractscataracts subtypeage-related cataractsmachine learning prediction |
| spellingShingle | Adriana Ivanescu Simona Popescu Adina Braha Bogdan Timar Teodora Sorescu Sandra Lazar Romulus Timar Laura Gaita Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study Medicina diabetes cataracts cataracts subtype age-related cataracts machine learning prediction |
| title | Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study |
| title_full | Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study |
| title_fullStr | Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study |
| title_full_unstemmed | Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study |
| title_short | Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study |
| title_sort | diabetes and cataracts development characteristics subtypes and predictive modeling using machine learning in romanian patients a cross sectional study |
| topic | diabetes cataracts cataracts subtype age-related cataracts machine learning prediction |
| url | https://www.mdpi.com/1648-9144/61/1/29 |
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