MACHINE LEARNING ALGORITHMS FOR PREDICTING FACTITIOUS DISORDER USING THE LEARNING VECTOR QUANTIZATION METHOD
Seta Samsiana*, Syamsul Arifin

Physical Engineering Study Program, Faculty of Industrial Technology and Systems Engginering, Sepuluh Nopember Institute of Technology Surabaya

Abstract

Factitious disorder is classified as a mental disorder. A person who has factitious disorder intentionally produces symptoms of the disease for the purpose of receiving attention and receiving medical treatment. The action of people with factitious disorder, aims to attract the sympathy and attention of others. Diagnosing factitious disorder is very difficult. The reason is, the sufferer looks fine. The doctor must eliminate any physical and mental illness before confirming the diagnosis of factitious disorder. Along with the development of machine learning technology. Incorporation of patient data and the use of machine learning technology can help detect the disease. The study was conducted using primary data and secondary data such as interviews, questioners. The method for diagnosing factitious disorder uses the Learning Vector Quantization method whether a person is a sufferer or not. Based on testing, 70% of the achievement of factitous detection accuracy was obtained

Keywords: Machine Learning, Factitious Disorder, Learning Vector Quantization

Topic: Engineering