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Published on: March 13, 2021
Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet
Riyad Alshammari1, Noorah Atiyah2, Tahani Daghistani1
1Health Informatics Department, College of Public Health and Health Informatics King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS) King Abdullah International Medical Research Center (KAIMRC) Ministry of National Guard Health Affairs, Riyadh, KSA.
Abstract:
Diabetes is a salient issue and a significant health care concern for many nations. The forecast for the prevalence of diabetes is on the rise. Hence, building a prediction machine learning model to assist in the identification of diabetic patients is of great interest. This study aims to create a machine learning model that is capable of predicting diabetes with high performance. The following study used the BigML platform to train four machine learning algorithms, namely, Deepnet, Models (decision tree), Ensemble and Logistic Regression, on data sets collected from the Ministry of National Guard Hospital Affairs (MNGHA) in Saudi Arabia between the years of 2013 and 2015. The comparative evaluation criteria for the four algorithms examined included; Accuracy, Precision, Recall, F-measure and PhiCoefficient. Results show that the Deepnet algorithm achieved higher performance compared to other machine learning algorithms based on various evaluation matrices.
