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Detecting Diabetes in Human Body using Different Machine Learning Techniques

One of the most widespread and chronic diseases in our world today is diabetes. Human suffers from an increased amount of blood glucose in the body caused due to inadequate insulin production. Early detection of diabetes is thus now become a must to combat and take preventive actions against the potential risks of this disease. In this paper, we present a methodology for the classification of diabetics using different machine learning models (Decision Tree, K-Nearest Neighbors, Naïve Bayes, Random Forest) to analyze diabetes patients and detect diabetes in human bodies. The proposed methodology is evaluated on Diabetes Dataset namely Pima Indians Diabetes (PIMA) Dataset. Initially, we apply the different preprocessing techniques to prepare data for the experiment. The extracted training data are passed through each model. For measuring the performance of the classifiers, we apply the measurements of sensitivity, precision, f1-score, and overall accuracy. However, in the experiment, we see that the Random Forest model is achieved the highest score with an accuracy of 86%. We hope that these classification techniques as per our study can be combined with real-time data with the help of the “Internet of Things” to make real-time devices for health care applications.