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Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting

Diabetes is one of the most serious diseases which is becoming increasingly common in recent years. Diabetes can be treated and its consequences are prevented or delayed if predicted timely. This paper investigates an evolutionary computation approach for diabetes prediction. By utilizing the multi-objective Genetic Programming Symbolic Regression, the prediction accuracy level of 79.17% is achieved. Two utilized objectives are namely prediction accuracy and complexity level of the created model (i.e., formula). Moreover, a majority-voting scheme is proposed and compared with other conventional classification algorithms. A widely studied dataset for diabetes prediction, the Pima Indian Diabetes dataset shared in University of California Irvine dataset repository, has been selected for conducting our experimental studies. The work presented here has profound implications for future applications of diabetes prediction and may one help to solve the problem of diabetes by their timely prediction.