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Journals / Psikiyatride Güncel Yaklaşımlar / 2026 / Cilt: 18 Sayı: 3

Machine Learning in Family Research: A Systematic Review

Aile Araştırmalarında Makine Öğrenmesi: Sistematik Bir Gözden Geçirme

Gizem Kavalcı(ADNAN MENDERES ÜNİVERSİTESİ)0000-0002-6941-7360
Gözde Sayın Karakaş(ADNAN MENDERES ÜNİVERSİTESİ)0000-0002-9132-4144
Pages
949–966
Publication Date
2026-04-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

By doing so, machine learning techniques can be used more effectively to understand family dynamics and support individuals' mental health.

📌 3 Key Takeaways

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Objective & Hypothesis:Machine learning is a powerful tool for extracting meaningful patterns from large datasets and performing predictive modeling.
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Key Findings & Data:A comprehensive literature search was conducted in the Web of Science, PubMed, Scopus, Science Direct, Ulakbim, and TRDizin databases, and 11 studies meeting the PICOS criteria were analyzed.
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Conclusion & Impact:By doing so, machine learning techniques can be used more effectively to understand family dynamics and support individuals' mental health.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Makine öğrenmesi · aile dinamikleri · ruh sağlığı

Abstract

Machine learning is a powerful tool for extracting meaningful patterns from large datasets and performing predictive modeling. In recent years, machine learning methods have been increasingly applied in family sciences, mental health, and educational research. This systematic review aims to evaluate how machine learning methods are used to understand the impact of family dynamics on individuals’ mental health, educational attainment, and behavioral outcomes. A comprehensive literature search was conducted in the Web of Science, PubMed, Scopus, Science Direct, Ulakbim, and TRDizin databases, and 11 studies meeting the PICOS criteria were analyzed. The reviewed studies indicate that machine learning algorithms provide strong predictions in areas such as domestic violence, depression, academic achievement, and children’s psychosocial development. In particular, Random Forest (RF), Support Vector Machines (SVM), deep learning, and natural language processing (NLP) methods have demonstrated high accuracy in predictive tasks. However, challenges related to model transparency, ethical concerns, and applicability within the family context remain among the limitations of machine learning models. Therefore, future research should focus on enhancing the interpretability of machine learning approaches, integrating them with theoretical models, and supporting their application in family sciences with more empirical studies. By doing so, machine learning techniques can be used more effectively to understand family dynamics and support individuals' mental health.

Özet

Makine öğrenmesi, büyük veri setlerinden anlamlı örüntüler çıkarmak ve tahmine dayalı modelleme yapmak için kullanılan güçlü bir araçtır. Son yıllarda, makine öğrenmesi yöntemleri aile bilimleri, ruh sağlığı ve eğitim araştırmalarında giderek daha fazla uygulanmaktadır. Bu sistematik gözden geçirme, makine öğrenmesi yöntemlerinin aile dinamikleri, bireylerin ruh sağlığı, eğitim başarısı ve davranışsal sonuçları üzerindeki etkilerini anlamada nasıl kullanıldığını değerlendirmeyi amaçlamaktadır. Çalışmada, Web of Science, PubMed, Scopus, Science Direct, Ulakbim ve TRDizin veri tabanlarında kapsamlı bir literatür taraması yapılmış ve PICOS kriterlerine uygun 11 çalışma analiz edilmiştir. İncelenen çalışmalar, makine öğrenmesi algoritmalarının aile içi şiddet, depresyon, akademik başarı ve çocukların psikososyal gelişimi gibi konularda güçlü öngörüler sunduğunu ortaya koymaktadır. Özellikle Rasgele Orman (RF), Destek Vektör Makineleri (SVM), derin öğrenme ve doğal dil işleme (NLP) yöntemlerinin yüksek doğruluk oranlarıyla tahmin yapabildiği görülmektedir. Ancak, model şeffaflığı, etik kaygılar ve aile bağlamında uygulanabilirlik gibi konular, makine öğrenmesi modellerinin sınırlılıkları arasında yer almaktadır. Bu nedenle, gelecekteki araştırmalarda, makine öğrenmesi yaklaşımlarının daha açıklanabilir hale getirilmesi, kuramsal modellerle entegre edilmesi ve aile bilimleri alanında daha fazla ampirik araştırma ile desteklenmesi önerilmektedir. Böylece, makine öğrenmesi teknikleri aile içi dinamikleri daha iyi anlamak ve bireylerin ruh sağlığını desteklemek için daha etkin bir şekilde kullanılabilir.

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Cite This Article

Kavalcı, G., Karakaş, G. S. (2026). Machine Learning in Family Research: A Systematic Review. Psikiyatride Güncel Yaklaşımlar, Cilt: 18 Sayı: 3, 949–966. https://doi.org/10.18863/pgy.1663586

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