Diabetes prediction model using data mining techniques

Diabetes is the leading cause of death in the world, and it also affects kidney disease, loss of vision, and heart disease. Data mining techniques contribute to health care decisions for accurate disease diagnosis and treatment, reducing the workload of experts. Diabetes prediction is a rapidly expanding field of research. Early diabetes prediction will result in improved treatment. Diabetes causes a variety of health issues. Therefore, it is critical to prevent, monitor, and raise awareness about it. Type 1 and Type 2 diabetes can cause heart disease, renal problems, and eye difficulties. In this paper, we propose a diabetes prediction model using data mining techniques. We apply four data mining techniques such as Random Forest, Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. The proposed mechanism is trained using Python and analysed with a real dataset, which is collected from Kaggle. Furthermore, the performance of the proposed mechanism is analysed using the confusion matrix, sensitivity and accuracy performance metrices. In logistic regression, the accuracy is high, i.e., 82.46%, in comparison to other data mining techniques.