Abstract
Diabetes is a chronic disease caused by a deficiency of insulin that is prevalent around the world. Although doctors diagnose diabetes by testing glucose levels in the blood, they cannot determine whether a person is diabetic on this basis alone. Classification algorithms are an immensely helpful approach to accurately predicting diabetes. Merging two algorithms like the K-Nearest Neighbor (K-NN) Algorithm and the Genetic Algorithm (GA) can enhance prediction even more. Choosing an optimal ratio of crossover and mutation is one of the common obstacles faced by GA researchers. This paper proposes a model that combines K-NN and GA with Adaptive Parameter Control to help medical practitioners confirm their diagnosis of diabetes in patients. The UCI Pima Indian Diabetes Dataset is deployed on the Anaconda python platform. The mean accuracy of the proposed model is 0.84102, which is 1% better than the best result in the literature review.
Highlights
The world is facing many prevalent and chronic diseases
Research paper [5] employed the Pima Indian Diabetes Dataset to measure the performance of combined K-Nearest Neighbor (K-NN) and Genetic Algorithm (GA) algorithms
The present researchers implemented the PIMA Dataset to calculate the accuracy of the combined K-NN and GA model
Summary
The world is facing many prevalent and chronic diseases. Diabetes is one of them. Data were taken from patients and experts is the most important factor [3]. If these data are classified and predicted in a precise way, the global health expenditure can be reduced by up to 10% (760 billion USD) [1]. Data mining has three steps: exploration, pattern identification, and deployment [4]. Classifying the dataset is one of the most popular techniques in data mining. It employs a set of pre-classified examples for developing a model that can classify the records population in general. With the help of classification algorithms, diabetes can be diagnosed more accurately [4]
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