Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India.
Department of Ophthalmology, SRM Medical College Hospital and Research Centre, Kattankulathur, Chennai, TN, India.
J Med Syst. 2023 Aug 8;47(1):85. doi: 10.1007/s10916-023-01976-7.
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.
糖尿病性视网膜病变（DR）是导致糖尿病患者视力受损甚至生命威胁的一种疾病。尤其是 II 型糖尿病患者，他们更容易出现视网膜问题。因此，对 DR 进行早期预测对于防止糖尿病患者视力受损是非常必要的。本可行性研究的主要目的是确定可能导致糖尿病性视网膜病变的最关键的风险特征。本研究调查了 II 型糖尿病患者的社会分析、糖尿病、行为和临床风险因素。我们对所有参与者进行了一次自我个体问卷调查。我们的问卷询问了结果的可靠性、在筛查测试过程中的舒适度、参与未来筛查的意愿、总体看法以及对 DR 筛查测试的满意度。我们提出了一种随机森林模型，用于预测糖尿病患者中 DR 风险的患病率。我们使用更强大的 SHAP 可解释人工智能 (XAI) 工具进一步解释了模型。SHAP 方法可以了解输入变量如何与其代表性输出记录相互作用，以及输入变量如何进行排序。此外，我们对数据进行了各种描述性和推断性统计分析，并通过假设检验评估了上述因素之间的显著关系。这项可行性研究共涉及 172 名 II 型糖尿病患者（73 名男性和 99 名女性）。因此，我们发现 172 名参与者中有 81 名（47.09%）患有可参考的 DR。患者的平均年龄为 55.08 岁，标准差为±9.770（范围为 40 至 79）。II 型患者受到轻度、中度、重度和晚期增生性糖尿病视网膜病变（PDR）的影响，分别占总样本的 23.83%、13.95%、5.81%和 3.48%。使用临床数据集，开发的 RF 模型获得了 94.9%的高精度。我们的结果表明，II 型糖尿病患者中，年龄较大、血糖水平异常和糖尿病持续时间较长的患者会出现微小微小型病变。
