Performance Analysis and Comparison of Machine Learning Models in Early Diabetes Prediction

There are many chronic diseases nowadays affecting several lives and one such disease is Diabetes Mellitus, commonly known as Diabetes. Also, keeping in mind the importance of health and quality of life, it is important to learn about diseases. Diabetes is no longer counted as a disease but a main lifestyle issue that has come into progression due to the modern lifestyle change of people. This disease, if ignored for a longer time, can be a threat to the person's life. Hence, advanced detection of this disease becomes particularly important, which is possible now with scientific research. Machine Learning has positively impacted the health sector by giving different technologies for predicting different diseases and has provided the medical industry with many innovations. This study shows the result for the same as in this, a comparative study between Decision Tree, Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) has been made to predict diabetes. As a result, we would be checking which algorithm is more accurate to predict diabetes. According to this study, the model that gives the highest accuracy of 99% is random forest classifier.