Diagnosis of Heart Disease Using Generalized Learning Vector Quantization (GLVQ) and Genetic Algorithms Methods

Abstract

Coronary heart disease is one of the diseases that contributes quite high rates of death in the world. The World Heart Federation estimates that the number of deaths due to coronary heart disease in Southeast Asia reached 1.8 million cases in 2014. In 2013 there were at least 883,447 people diagnosed with coronary heart disease in Indonesia with the majority of patients aged 55-64 years. The death rate due to heart disease is quite high, which is about 45 percent of all deaths in Indonesia. Therefore this study was conducted in the hope of reducing the number of deaths caused by heart disease and the concrete steps that could be made to the diagnosis results by the system.

In this study using a combination of two methods, namely Genetic Algorithms and Generalized Learning Vector Quantization (GLVQ). The combination of these methods is done to get the optimal weight in the training process which later the weight is used to get the classification results in the testing process

From the test results obtained an average accuracy 71.50% with the best parameters namely learning rate 0.02, reduction learning rate (dec ?) 0.9, epoch 100, population number 30, number of generations 20, crossover rate 0.2, and mutation rate 0.1.