Development of Data Classification using a Hybrid Method of Adaptive Artificial Neural Networks and Particle Swarm Optimization for IdentifyingPatients at Risk of Diabetes

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Akanit Thongjit
Poonpong Suksawang
Jatupat Mekparyup


               This research aimed to 1) develop a method of data classification using Adaptive Artificial Neural Network and Particle Swarm Optimization (AANN-PSO),2) comparethe performance of the developed data classification method with three types: Adaptive Artificial Neural Network and Particle Swarm Optimization (AANN-PSO), Artificial Neural Network and Particle Swarm Optimization (ANN-PSO) and Artificial Neural Network (ANN) and 3), classifythe patients who are at risk of diabetes and normal subjects with the method of Adaptive Artificial Neural Network and Particle Swarm Optimization (AANN-PSO). The data set involved 7,000 patients who were at risk of diabetes, in the area under the responsibility of the Nakhon Phanom Provincial Health Office in the year 2018. The research results were as follows:

            1. The data classification using Adaptive Artificial Neural Network and Particle Swarm Optimization(AANN-PSO)with the new conversion functionwhen acted to decrease the slope of the target function, while data classification performance increased.        

            2. Data classification using AANN-PSOresulted in better performance than ANN-PSOand ANNin all five situations. Furthermore, when the sample size was increased, the performance was even better.        

            3. Factors that affected the risk of diabetes included body mass index, diastolic blood pressure, age, systolic blood pressure, and a family history of diabetes. The classification of patients who are at risk of diabetes by using AANN-PSO had an accuracy of 92.79%, withmean square error of 0.07.

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บทความวิจัย (Research Articles)


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