Open AccessPredicting Effective Factors in Schizophrenia Using Data Mining
Author(s) -
Dinesh Mavaluru,
Jayabrabu Ramakrishnan,
Azath Mubarakali
Publication year - 2020
Publication title -
journal of research in science, engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2693-8464
DOI - 10.24200/jrset.vol7iss02pp29-33
Subject(s) - schizophrenia (object oriented programming) , support vector machine , decision tree , data mining , computer science , machine learning , artificial neural network , artificial intelligence , field (mathematics) , data science , mathematics , pure mathematics , programming language
Data mining is a technique for discovering new knowledge from databases and the use of data mining in medicine is considered one of the most widely used fields of data mining. Schizophrenia is one of the most common illnesses that cause many financial and social damages to society due to the loss of individual performance. In this study, we will examine the most effective fields of predictor in schizophrenia and then predict the age of occurring schizophrenia. In this study, some common classification methods such as support vector machine, decision tree and neural network have been used. The results show that the support vector machine model has more efficiency than other models.