(untitled) (JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences, 2022)
Background: Diabetes is one of the dangerous and silent illnesses that cause sudden death. It can occur at any time and may cause great injury to the organs of the body or damage them completely. So, we must investigate this disease at the beginning of its appearance and before it gets hard to treat. With the fast advancement of Machine Learning (ML), these approaches enhanced the efficiency of decision processes in a wide range of applications, including medical diagnostics. Materials and Methods: In this paper, we chose a medical application field and used supervised machine learning algorithms to construct a high-accuracy prediction model for diabetes in humans at an early stage, before it progresses to the point of morbidity or fatality. The suggested model can extract hidden knowledge from diabetes-related data gathered from the Kaggle machine learning repository. We utilize Microsoft Azure ML Studio to model these ML algorithms. Results: This study will benefit the health industry by offering users an online tool (i.e., web service) that allows them to input data and receive results that predict whether or not the person has diabetes. As a result, prior knowledge and ongoing monitoring of their diabetic health state will lower the risk of complications, morbidity, and death caused by this illness. After running numerous experiments with the classifier models to evaluate the proposed system, several performance indicators, including Recall, Precision, Accuracy, and f1-score, are measured for comparison. Based on the classification output, it was determined that Decision Forest is a better strategy and produces better results than the other ML approaches. Conclusion: The suggested system’s key contribution is to improve healthcare quality, minimize hospitalizations, and lower the high expenditures of healthcare and drugs.