Machine Learning Based Unified Framework for Diabetes Prediction
Machine learning gained a significant position in healthcare services (HCS) due to its ability to improve the disease prediction in HCS. Machine learning techniques and artificial intelligence have already been worked in the HCS area. Recently, diabetes is a notable public chronic disease worldwide. It is growing rapidly because of bad lifestyles, taking more junk food and also lake of health awareness. Therefore, there is a need of framework that can effectively track and monitor people's diabetes and health condition within an application view. In this study, we proposed a framework for real time diabetes prediction, monitoring and application (DPMA). Our objective is to develop an optimized and efficient machine learning (ML) application which can effectually recognize and predict the condition of the diabetes. In this work, five most important machine learning classification techniques were considered for predicting diabetes. However, we use different evaluation criteria to investigate the performance of these classification techniques. In addition, performance measurement of the classification techniques was evaluated by applying the 10-fold cross validation method. The analysis results show that Naïve Bayes achieved highest performance than the other classifiers, obtaining the F1 measure of 0.74.
S. M. Hasan Mahmud, Md Altab Hossin, Md Altab Hossin, Sheak Rashed Haider Noori, Md Nazirul Islam Sarkar