Depresyonda motor aktivitenin makine öğrenmesi ile değerlendirilmesi

Year 2024, , 49 - 59, 15.01.2024

Selim Aras , Neriman Aras , Mustafa Alptekin Engin

https://doi.org/10.28948/ngumuh.1351103

Abstract

Psikiyatrik hastalıkların neredeyse tümünde olduğu gibi depresyonun da klinik olarak değerlendirilmesi gözleme ve subjektif hasta şikâyetlerine dayanmaktadır. Psikomotor retardasyon (gerileme) depresyonun önde gelen semptomlarından biridir ve bunun göstergesi olarak depresyonlu hastalarda fiziksel aktivite azalır. Bu çalışmada, depresyonu olan ve olmayan bireylerin günlük fiziksel aktivite verileri ile oluşturulmuş bir veri setini referans olarak kullanarak, depresyon tanısı için makine öğrenimi temelli objektif bir tanı destekleyici yöntem geliştirmek amaçlanmıştır. Geniş bir öznitelik araştırması yapıldıktan sonra, Fisher Öznitelik Seçimi ile en iyi dört öznitelik belirlenmiş ve Toplu Torbalı Ağaç yöntemini kullanarak 0,88 doğruluk ile referans çalışmasından daha iyi bir sınıflandırma sonucu elde edilmiştir. Ayrıca, referans çalışma ile karşılaştırmak için sınırlandırılan dört öznitelikten daha fazlası seçildiğinde doğruluğun 0,90’nın üzerine çıktığı belirlenmiştir. Böylece, fiziksel aktivite verilerini kullanarak geliştirilen makine öğrenimi temelli yöntemle depresyonu olan ve olmayan bireyleri yüksek doğruluk payı ile ayırt edilmesi başarılmıştır. Bu çalışma, aktivite verilerinin depresyonda tanı destekleyici bir araç olarak kullanılabileceğine dair umut verici sonuçlar ortaya koymuştur. Elde edilen sonuçlar, fiziksel aktivite gibi farklı biyobelirteçlerin de makine öğrenimi ile birlikte kullanıldığında, psikiyatrik değerlendirmedeki objektif tanı destekleyici kriterlerin eksikliğini giderebilecek potansiyele sahip olduğunu göstermektedir.

Keywords

Makine öğrenmesi, öznitelik, depresyon, fiziksel aktivite, psikomotor retardasyon

Evaluation of motor activity in depression with machine learning

Abstract

The clinical assessment of depression is based on observation and subjective patient complaints of almost all psychiatric disorders. Psychomotor retardation is one of the leading symptoms of depression, and as an indicator of this, physical activity is reduced in depressed patients. In this study, we aimed to develop a machine learning-based objective diagnostic support method for diagnosing depression using a dataset of daily physical activity data of individuals with and without depression as a reference. After detailed feature searches, we identified the four best features by Fisher Feature Selection. Using the Ensemble Bagged Tree method, we achieved a better classification result than the reference study, with an accuracy of 0.88. In addition, we found that the accuracy exceeded 0.90 when more than the four features we limited to compare with the reference study were selected. Thus, we could distinguish between individuals with and without depression with high accuracy with the machine learning-based method, which we developed using physical activity data. This study has shown promising results that activity data can be used as a diagnostic tool for depression. Our results show that different biomarkers, such as physical activity, when used with machine learning, can potentially overcome the lack of objective diagnostic support criteria in psychiatric evaluation.

Keywords

Machine learning, feature, depression, physical activity, psychomotor retardation

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