Medical systems incorporate modern computational intelligence in healthcare. Machine learning techniques are applied to predict the onset and reoccurrence of the disease, identify biomarkers for survivability analysis depending upon certain health conditions of the patient. Early prediction of diseases like diabetes is essential as the number of diabetic patients of all age groups is increasing rapidly. To identify underlying reasons for the onset of diabetes in its early stage has become a challenging task for medical practitioners. Continuously increasing diabetic patient data has necessitated for the applications of efficient machine learning algorithms, which learns from the trends of the underlying data and recognizes the critical conditions in patients. In this article, an ensemble-based framework named e DiaPredict is proposed. It uses ensemble modeling, which includes an ensemble of different machine learning algorithms comprising XGBoost, Random Forest, Support Vector Machine, Neural Network, and Decision tree to predict diabetes status among patients. The performance of eDiaPredict has been evaluated using various performance parameters like accuracy, sensitivity, specificity, Gini Index, precision, area under curve, area under convex hull, minimum error rate, and minimum weighted coefficient. The effectiveness of the proposed approach is shown by its application on the PIMA Indian diabetes dataset wherein an accuracy of 95% is achieved.

中文翻译：

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eDiaPredict：基于集合的糖尿病预测框架

医疗系统将现代计算智能融入医疗保健中。机器学习技术用于预测疾病的发作和复发，根据患者的某些健康状况识别用于生存能力分析的生物标志物。随着各个年龄段的糖尿病患者数量迅速增加，对糖尿病等疾病的早期预测至关重要。找出糖尿病早期发病的根本原因已成为医学从业人员面临的一项艰巨任务。不断增加的糖尿病患者数据需要高效的机器学习算法的应用，该算法从基础数据的趋势中学习并识别患者的危急情况。在本文中，一个名为 e 的基于集成的框架直径预测被提议。它使用集成建模，其中包括一组不同的机器学习算法，包括 XGBoost、随机森林、支持向量机、神经网络和决策树，以预测患者的糖尿病状态。的表现eDiaPredict已使用各种性能参数进行评估，如准确性、灵敏度、特异性、基尼指数、精度、曲线下面积、凸包下面积、最小错误率和最小加权系数。所提出方法的有效性通过其在 PIMA 印度糖尿病数据集上的应用显示，其中实现了 95% 的准确度。