Volume 6, Issue 1 (Spring 2019)
jhbmi 2019, 6(1): 12-23 |
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Momeny M, Latif A M, Agha Sarram M, Hajmirzazade K, Gharravi S, Seyed Mahammad N. Diabetes Prediction by Optimizing the Nearest Neighbor Algorithm Using Genetic Algorithm. jhbmi 2019; 6 (1) :12-23
URL: http://jhbmi.ir/article-1-311-en.html
URL: http://jhbmi.ir/article-1-311-en.html
Mohammad Momeny * 
, Ali Mohammad Latif , Mehdi Agha Sarram , Kazem Hajmirzazade , Sorayya Gharravi , NaghiboAlghara Seyed Mahammad
, Ali Mohammad Latif , Mehdi Agha Sarram , Kazem Hajmirzazade , Sorayya Gharravi , NaghiboAlghara Seyed Mahammad
Ph.D. Student, Electrical and Computer Engineering Dept., School of Electrical and Computer Engineering, Yazd University, Yazd, Iran
Abstract: (6628 Views)
Introduction: Diabetes or diabetes mellitus is a metabolic disorder in body when the body does not produce insulin, and produced insulin cannot function normally. The presence of various signs and symptoms of this disease makes it difficult for doctors to diagnose. Data mining allows analysis of patients’ clinical data for medical decision making. The aim of this study was to provide a model for increasing the accuracy of diabetes prediction.
Method: In this study, the medical records of 1151 patients with diabetes were studied, with 19 features. Patients’ information were collected from the UCI standard database. Each patient has been followed for at least one year. Genetic Algorithm (GA) and the nearest neighbor algorithm were used to provide diabetes prediction model.
Results: It was revealed that the prediction accuracy of the proposed model equals 0.76. Also, for the methods of Naïve Bayes, Multi-layer perceptron (MLP) neural network, and support vector machine (SVM), the prediction accuracy was 0.62, 0.65, and 0.75, respectively.
Conclusion: In predicting diabetes, the proposed model has the lowest error rate and the highest accuracy compared to the other models. Naïve Bayes method has the highest error rate and the lowest accuracy.
Method: In this study, the medical records of 1151 patients with diabetes were studied, with 19 features. Patients’ information were collected from the UCI standard database. Each patient has been followed for at least one year. Genetic Algorithm (GA) and the nearest neighbor algorithm were used to provide diabetes prediction model.
Results: It was revealed that the prediction accuracy of the proposed model equals 0.76. Also, for the methods of Naïve Bayes, Multi-layer perceptron (MLP) neural network, and support vector machine (SVM), the prediction accuracy was 0.62, 0.65, and 0.75, respectively.
Conclusion: In predicting diabetes, the proposed model has the lowest error rate and the highest accuracy compared to the other models. Naïve Bayes method has the highest error rate and the lowest accuracy.
Keywords: Prediction of diabetes, Genetic algorithm, Nearest neighbor algorithm, Data mining
eprint link: http://eprints.kmu.ac.ir/id/eprint/31282
eprint link: http://eprints.kmu.ac.ir/id/eprint/31282
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2018/05/16 | Accepted: 2018/09/29
Received: 2018/05/16 | Accepted: 2018/09/29
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