A robust voting approach for diabetes prediction using traditional machine learning techniques

The noteworthy advances in biotechnology and biomedical sciences have prompted a huge creation of information, for example, high throughput genetic information and clinical data, produced from extensive Electronic Health Records. To this end, utilization of machine learning and data mining techniques in biosciences is by and by crucial and fundamental in endeavors to change cleverly all accessible data into profitable knowledge. Diabetes mellitus is characterized as a gathering of metabolic issue applying critical weight on human health around the world. Broad research in all parts of diabetes (determination, pathophysiology, treatment, and so forth.) has prompted the age of tremendous measures of information. The point of the present examination is to direct an orderly audit of the uses of machine-learning, data mining strategies and instruments in the field of diabetes. The main theme of this work is to provide a system which can prognosticate the diabetes in patients with better accuracy. Here, eleven well-known machine-learning algorithms like Naive Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting etc. are used for detection of diabetes at an early stage. The evaluations of all the eleven algorithms are examined on various parameters like accuracy, precision, F-measure and recall. After cross-validation and hyper-tuning, the best three machine-learning algorithms are determined and then used in Ensemble Voting Classifier. The experimental results affirm that the pointed framework can accomplish to outstanding outcome of almost 86% accuracy of the Pima Indians Diabetes Database.