Integrating Machine Learning with Web Application to Predict Diabetes

Diabetes is one of the highest causes of death in the world. Diabetes is caused when theblood glucose level is too high in the body. Gradually, high blood glucose leads to heartdisease, stroke, eye, and foot problems. To prevent the dreadful effects among people,early detection is required that would lead to proper medical treatment and change inlifestyle. Therefore, with the rise of machine learning we can predict if a patient hasdiabetes or not. Furthermore, we will integrate the trained model to a web applicationthat will connect the model to generate predictions in real-time considering factorsresponsible for diabetes like body mass index (BMI), age, insulin, etc. In this paper, we areusing the Pima Indian dataset that is originally from the National Institute of Diabetes,Digestive and Kidney Diseases for diabetes prediction model design using machinelearning. The proposed system in this paper is the Soft Voting ensemble classifier. Thealgorithm with the best accurate result was used in making predictions. This model wasdeployed to the web using flask (a python framework), it takes inputs from the user tomake predictions. This model is implemented using python programming language andflask (a web base framework) hosted in GCP. Soft Voting ensemble classifiers evenperform better than other classifiers with an accuracy of 91.55% which is quite promisingconsidering the other classification models in the literature for this problem.