Veri Odaklı Yaklaşımlar ile Zihinsel İyi Oluş Durumunun Tespiti

Yıl 2025, Cilt: 11 Sayı: 1, 17 - 29, 30.06.2025

Aysun Kazak Saltı , Evrim Ersin Kangal

https://doi.org/10.29132/ijpas.1619385

Öz

Ruhsal iyi oluş düzeyi bozuklukları, modern yaşam biçimlerinin en önemli sorunları arasında yer almaktadır ve hastalıkların erken teşhisinin etkili bir önleme için ne kadar önemli olduğu artık bilinen bir gerçektir. Diğer yandan, Makine Öğrenimi (MÖ) algoritmaları günümüzde hastalık tespitinde değerli bir rol oynamaktadır. Bu çalışmanın amacı yalnızca çeşitli ML sınıflandırıcılarının performansını araştırmak değil aynı zamanda ruh sağlığı tanısı için modern bir teknik sunmaktır. Bu bağlamda, araştırmamızda literatürde yaygın olarak bilinen ML sınıflandırıcılarından Bootstrap Aggregating (Bagging), Extremely Randomized Trees (ExtraTrees), Passive-Aggressive, Light Gradient Boosting Machine (LGBM), Perceptron ve Stochastic Gradient Descent (SGD) algoritmaları ele alınmıştır. Seçilen bireylerde ruhsal sağlık hastalıklarına katkıda bulunan faktörleri ele almak için üç aşamalı bir veri işleme yaklaşımı kullanıyoruz: parçalara ayırma, özellik çıkarımı ve sınıflandırma. Ele alınan veri setindeki özellik önemini analiz eden araştırmamız, yaşın, aile geçmişinin ve işyeri ortamının bir çalışanın ruh sağlığı durumu üzerinde önemli bir etkiye sahip olduğunu ortaya koymaktadır.

Anahtar Kelimeler

Makine Öğrenme , Sınıflandırıcı , Sağlık

Etik Beyan

Veriler herkese açık bir veri tabanından elde edildiğinden etik kurul değerlendirmesine gerek yoktur.

Detection of Mental Well-Being Status Through Data-Driven Approaches

Öz

Mental well-being disorders are among the most significant challenges in the modern lifestyles, and it is well-established that early detection of diseases is essential for effective prevention. On the other hand, Machine Learning (ML) algorithms currently play a valuable role in disease detection. The aim of this study is not only to investigate the performance of various ML classifiers but also to propose a modern technique for mental health diagnosis. In this context, our research considers Bootstrap Aggregating (Bagging), Extremely Randomized Trees (ExtraTrees), Passive-Aggressive, Light Gradient Boosting Machine (LGBM), Perceptron, and Stochastic Gradient Descent (SGD) algorithms, which are among the widely recognized ML classifiers in literature. To address the factors contributing to mental health illnesses among the selected individuals, we employ a three-phase data processing approach: segmentation, feature extraction, and classification. Analyzing feature importance from the selected dataset, our study highlights the significant impact of age, family history, and workplace environment on a worker's mental health status.

Anahtar Kelimeler

Machine Learning , Classifier , Health

Etik Beyan

The data is sourced from an open-access database, so there is no need for an ethics committee’s evaluation.

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