A Comparative Analysis and Risk Prediction of Diabetes at Early Stage using Machine Learning Approach

  • Ahmed Kareem, Lei Shi, 3Lin Wei, Yongcai Tao


Nowadays, diabetes is one of the fastest growing chronic life threatening diseases has become a common disease to the mankind from young to the old persons.The growth of the diabetic patients that has already affected 422 million people worldwide according to the report of World Health Organization (WHO), in 2018, now also it is increasing day by-day due to various causes such as bacterial or viral infection, toxic or chemical contents mix with the food, auto immune reaction, obesity, bad diet, change in lifestyles, eating habit, environment pollution, etc. Hence, diagnosing the diabetes is very essential to save the human life from diabetes. Around 50% of all people suffering from diabetes are undiagnosed because of its long-term asymptomatic phase is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the body’s cells do not respond properly to insulin. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. Moreover, this paper presents a diabetes prediction system to diagnosis diabetes and to improve the accuracy in diabetes prediction using medical data with various machine learning algorithms.Finally, the result shows the Multilayer Perceptron (MLP) algorithm and the Radial Basis Function Network (RBF/RBFN) has the highest specificity of 95% and 98.72%, respectively holds best for the analysis of diabetic data. Using tenfold Cross- Validation evaluation techniquesRadial Basis Function Network outcome states the best accuracy of 98.80%.