Identifying the Best Classifiers for Numerical Healthcare Data for Diabetes Prediction

In the field of ML-Machine Learning, classification is one of the most widely used prediction tasks. In recent era, ML is being widely deployed in almost every field of real-world applications including heathcare. When we use ML for healthcare applications, it should be our main goal to achieve highest possible accuracy. Accuracy of any model is dependent on training dataset and algorithm being implemented. Different characteristics of training dataset contribute significantly to achieve highest possible accuracy. If we talk about general observations then the healthcare applications related data are mainly numerical like test reports showing numerical values. Classification is a categorical task that is easy to understand by patients like whether someone is having a particular disease or not. In this research work, we have evaluated and compared performances of various classifiers to decide which classifier works best when the training data is exclusively numerical. Based on our experiments, we have observed that Logistic Regression, Neural Network and Naive Bayes perform more accurately for exclusively numerical data to predict diabetes.