Prediction of diabetes using machine learning

Abstract

Machine learning is an application of artificial intelligence which has proved to be a major breakthrough in the field of medical sciences to take care of healthcare sector especially in diagnosing of diseases. In recent times, various studies have shown there is a high percentage of population across the worlds who are suffering from diabetes. It is quite well-known fact that due to high content of blood sugar levels in human beings results in such a metabolic disordered disease. The rapid growth of diabetes is well known reality in today’s world due to unhealthy lifestyles, processed food, lack of health awareness and proper physical exercises. It is important to note that diabetes can cause visual disturbances, pancreas malfunction, nerve damage, heart diseases, kidney damage, fatigue and lack of energy, excessive urination, gastroparesis, damaged blood vessels, foot problems, dry and cracked skin etc and few other chronic diseases. Therefore, it becomes necessary to detect and diagnose diabetes at an early stage. The proposed work in this research deals with the classification of people who are diagnosed with diabetes using Classification algorithms such as Logistic Regression (LR), Random forest, SVM, KNN, Gradient boosting (GB) and Decision tree(DT). The experiment showed that KNN algorithm gave better results than when compared with other classified algorithms. The results showed an accuracy of 85% was achieved.