Smart Diabetic Prediction: A Machine LearningApproach

With the tremendous advancement in information and communication techniques, the health care system is being transformed into smart healthcare systems. The large volume of patient data, such as Electronic Health Records, has shown the importance of new technologies like machine learning and artificial intelligence in advanced disease prediction. Diabetes Mellitus is a worldwide health disorder which needs to be addressed seriously, especially for elderly society. This is because it can be the first phase of many other serious diseases like heart attack, kidney disorder, etc. In this article, a smart diabetic monitoring/prediction system is proposed with different machine learning algorithms. To be precise, the risk of diabetes can be predicted well in advance on the basis of the patient health data and genetic conditions. This is where the role of machine learning and artificial intelligence comes in predicting the diseases. Diabetes diagnosis is usually done with patient data, such as blood glucose and random blood glucose from the daily examination. To predict degenerative diseases, machine learning methods such as random forest and support vector machines are used. Accuracy is one of the essential variables that should be examined during the disease diagnosis. In this article, we use a machine learning algorithm that can more accurately diagnose diabetes than the current ones. Also the efficiency of different existing algorithms will be compared with the proposed methodology.