Doğanın İnsan Yaşamı Üzerine Etkilerinin Karar Ağacı Algoritmaları İle İncelenmesi

Abstract

Aim: The aim of this study is to classify the obtained data correctly using machine learning algorithms. Material and Methods: Happiness, life satisfaction and hopelessness scales with personal information form were applied to 195 patients who came to the psychiatry clinic and wanted to receive psychological treatment due to their anxiety, depression and stress complaints. In this classification, theh happiness core was chosen as the dependent variable and the factors affecting this variable were determined by different methods such as training, test, and cross- validation. Results: KA-RF (0.9180) gave the most successful classification result among decision tree algorithms for k = 10 value. This result is supported by the criteria RMSE (0.2810), ROC area (0.9760) and MCC (0.8400). In addition, the variables that most affect the level of happiness or unhappiness of the participants in the study were found to be life satisfaction, age, and the ability to cope with difficulties, respectively. Conclusion: In line with the findings obtained as a result, it was determined that the effects of environmental and social factors as well as the positive effects of especially living spaces were found in the treatment of anxiety, depression and stress-related disorders.