Machine Learning in Family Research: A Systematic Review

Öz

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.

Anahtar Kelimeler

• Machine learning
• family dynamics
• mental health
• predictive modeling
• artificial intelligence

Aile Araştırmalarında Makine Öğrenmesi: Sistematik Bir Gözden Geçirme

Öz

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.

Anahtar Kelimeler

• Makine öğrenmesi
• aile dinamikleri
• ruh sağlığı
• tahmine dayalı modelleme
• yapay zeka

Kaynakça

1. Ahn E, Tejeda Y, Yang Y (2024) Examining fairness in machine learning applied to support families: A case study of preventive services. Fam Relat, 74:1285-1298.
2. Baldwin JR, Wang B, Karwatowska L, Schoeler T, Tsaligopoulou A, Munafò MR et al. (2023) Childhood maltreatment and mental health problems: a systematic review and meta-analysis of quasi-experimental studies. Am J Psychiatry, 180:117-126.
3. Bengio Y, Goodfellow I, Courville A (2015) Deep Learning. Cambridge, MIT Press.
4. Berkel C, Knox DC, Flemotomos N, Martinez VR, Atkins DC, Narayanan SS et al. (2023) A machine learning approach to improve implementation monitoring of family-based preventive interventions in primary care. Implement Res Pract, 4:e26334895231187906.
5. Bishop CM (1994) Neural networks and their applications. Rev Sci Instrum, 65:1803-1832.
6. Black JE, Kueper JK, Williamson TS (2023) An introduction to machine learning for classification and prediction. Fam Pract, 40:200-204.
7. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov, 2:121-167.
8. Chouldechova A, Benavides-Prado D, Fialko O, Vaithianathan R (2018) A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency:134-148. Jan 2018, New York, USA. Proceedings of Machine Learning Research.

9. Chung J, Teo J (2022) Mental health prediction using machine learning: taxonomy, applications, and challenges. Applied Computational Intelligence and Soft Computing, 2022:e9970363.
10. Cohen J (1962) The statistical power of abnormal-social psychological research: a review. J Abnorm Soc Psychol, 65:145-153.
11. Cohen J (1992) A power primer. Psychol Bull, 112:155-159.
12. Cohen J (1994) The earth is round (p<.05). Am Psychol, 49:997-1003.
13. De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81:3178-3192.
14. Dupont T, Kentish-Barnes N, Pochard F, Duchesnay E, Azoulay E (2024) Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach. Intensive Care Med, 50:114-124.
15. Fradkov AL (2020) Early history of machine learning. IFAC Pap OnLine, 53:1385-1390.
16. Gao T, Yang L, Zhou J, Zhang Y, Wang L, Wang Y et al. (2024) Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors. J Affect Disord, 356:483-491.
17. Garriga R, Mas J, Abraha S, Nolan J, Harrison O, Tadros G et al. (2022) Machine learning model to predict mental health crises from electronic health records. Nat Med, 28:1240-1248.
18. Gil M, Kim SS, Min EJ (2022) Machine learning models for predicting risk of depression in Korean college students: identifying family and individual factors. Front Public Health, 10:1023010.
19. Gomes SRBS, von Schantz M, Leocadio-Miguel M (2023) Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: a machine learning approach. Sleep Med, 102:123-131.
20. Harding JF, Keating B, Walzer J, Xing F, Zief S, Gao J (2022) How accurately can we predict repeat teen pregnancy based on social ecological factors? Dev Psychol, 58:1793.
21. Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer.
22. Ivaskevics K, Haller J (2022) Risk matrix for violent radicalization: a machine learning approach. Front Psychol, 13:745608.
23. Ioannidis JPA (2008) Why most discovered true associations are inflated. Epidemiology, 19:640-648.
24. James G, Witten D, Hastie T, Tibshirani R (2021) An Introduction to Statistical Learning. New York, Springer.
25. Joel S, Eastwick PW, Allison CJ, Arriaga XB, Baker ZG, Bar-Kalifa E et al. (2020) Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies. Proc Natl Acad Sci U S A, 117:19061-19071.
26. Kusuma K, Larsen M, Quiroz JC, Gillies M, Burnett A, Qian J et al. (2022) The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: a meta-analysis and systematic review. J Psychiatr Res, 155:579-588.
27. Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC et al. (2021) Machine learning and natural language processing in mental health: systematic review. J Med Internet Res, 23:e15708.
28. Li M, Li L, Wu F, Cao Y, Zhang H, Li X et al. (2021) Perceived family adaptability and cohesion and depressive symptoms: a comparison of adolescents and parents during COVID-19 pandemic. J Affect Disord, 287:255-260.
29. Li S, Liu M, Zhao N, Xue J, Wang X, Jiao D et al. (2021) The impact of family violence incidents on personality changes: an examination of social media users’ messages in China. Psych J, 10:598-613.
30. Lin S (2022) A clinician’s guide to artificial intelligence (AI): why and how primary care should lead the health care AI revolution. J Am Board Fam Med, 35:175-184.
31. Liu R (2022) Leveraging machine learning methods to estimate heterogeneous effects: father absence in China as an example. Chin Sociol Rev, 54:223-251.
32. Lopez-Larrosa S, Sánchez-Souto V, Losada DE, Parapar J, Barreiro Á, Ha AP et al. (2022) Using machine learning techniques to predict adolescents’ involvement in family conflict. Soc Sci Comput Rev, 41:1581-1607.
33. Mitchell TM (2010) Learning classifiers based on Bayes rule. Mach Learn, 1:1-17.
34. Mlandu C, Matsena-Zingoni Z, Musenge E (2023) Predicting the drop out from the maternal, newborn and child healthcare continuum in three East African Community countries: application of machine learning models. BMC Med Inform Decis Mak, 23:305.
35. Naar-King S, Ellis DA, Idalski Carcone A, Templin T, Jacques-Tiura AJ, Brogan Hartlieb K et al. (2016) Sequential multiple assignment randomized trial (SMART) to construct weight loss interventions for African American adolescents. J Clin Child Adolesc Psychol, 45:428-441.
36. Nixon RDV, King MW, Smith BN, Gradus JL, Resick PA, Galovski TE et al. (2021) Predicting response to cognitive processing therapy for PTSD: a machine-learning approach. Behav Res Ther, 144:103920.
37. Ongsulee P (2018) Artificial intelligence, machine learning and deep learning. In Proceedings of 2017 Fifteenth International Conference on ICT and Knowledge Engineering, 92-97. Bangkok, Thailand, Siam University.
38. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
39. Patrick EA, Fischer FP (1970) A generalized k-nearest neighbor rule. Information and Control, 16:128-152.
40. Rothenberg WA, Bizzego A, Esposito G, Lansford JE, Al-Hassan SM, Bacchini D et al. (2023) Predicting adolescent mental health outcomes across cultures: a machine learning approach. J Youth Adolesc, 52:1595-1619.
41. Quinlan JR (1986) Induction of decision trees. Mach Learn, 1:81-106
42. Sajjadian M, Lam RW, Milev R, Rotzinger S, Frey BN, Soares CN et al. (2021) Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis. Psychol Med, 51:2742-2751.
43. Salganik MJ, Lundberg I, Kindel AT, Ahearn CE, Al-Ghoneim K, Almaatouq A et al. (2020) Measuring the predictability of life outcomes with a scientific mass collaboration. Proc Natl Acad Sci U S A, 117:8398-8403.
44. Sankar A, Shen X, Colic L, Goldman DA, Villa LM, Kim JA et al. (2023) Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychol Med, 53:6656-6665.
45. Schwartz IM, York P, Nowakowski-Sims E, Ramos-Hernandez A (2017) Predictive and prescriptive analytics, machine learning and child welfare risk assessment: the Broward County experience. Child Youth Serv Rev, 81:309-320.
46. Silver D, Huang A, Maddison C, Guez A, Sifre L, Van Den Driessche G et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature, 529:484-489.
47. Simmons JP, Nelson LD, Simonsohn U (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci, 22:1359-1366.
48. Smith JD, Berkel C, Rudo-Stern J, Montaño Z, St. George SM, Prado G et al. (2018) The Family Check-Up 4 Health (FCU4Health): applying implementation science frameworks to the process of adapting an evidence-based parenting program for prevention of pediatric obesity and excess weight gain in primary care. Front Public Health, 6:293.
49. Sun X, Ram N, McHale SM (2020) Adolescent family experiences predict young adult educational attainment: a data-based cross-study synthesis with machine learning. J Child Fam Stud, 29:2770-2785.
50. Sun X, Sayer L (2024) Supervised machine learning for exploratory analysis in family research. J Marriage Fam, 86:1468-1494.
51. Suthaharan S (2016) Machine Learning Models and Algorithms for Big Data Classification. Cham, Springer.
52. Sutton RS, Barto AG (2018) Reinforcement Learning: An Introduction. Cambridge, MIT Press.
53. Syed S, Gonzalez-Izquierdo A, Allister J, Feder G, Li L, Gilbert R (2022) Identifying adverse childhood experiences with electronic health records of linked mothers and children in England: a multistage development and validation study. Lancet Digit Health, 4:e482-e496.
54. Tiyyagura G, Asnes AG, Leventhal JM, Shapiro ED, Auerbach M, Teng W et al. (2022) Development and validation of a natural language processing tool to identify injuries in infants associated with abuse. Acad Pediatr, 22:981-988.
55. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science, 185:1124-1131.
56. Wang C, Zhou T, Fu L, Xie D, Qi H, Huang Z et al. (2023) Risk and protective factors of depression in family and school domains for Chinese early adolescents: an association rule mining approach. Behav Sci (Basel), 13:893.
57. Vieira S, Liang X, Guiomar R, Mechelli A (2022) Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies. Clin Psychol Rev, 97:102193.
58. Yarkoni T (2009) Big correlations in little studies: inflated fMRI correlations reflect low statistical power—commentary on Vul et al. (2009). Perspect Psychol Sci, 4:294-298.
59. Yarkoni T, Westfall J (2017) Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci, 12:1100-1122.
60. Zheng Y, Ye Y (2022) Prediction of cognitive-behavioral therapy using deep learning for the treatment of adolescent social anxiety and mental health conditions. Sci Program, 2022:3187403.
61. Zhou SC, Zhou Z, Tang Q, Yu P, Zou H, Liu Q et al. (2024) Prediction of non-suicidal self-injury in adolescents at the family level using regression methods and machine learning. J Affect Disord, 352:67-75.
62. Zhong M, Zhang H, Yu C, Jiang J, Duan X (2022) Application of machine learning in predicting the risk of postpartum depression: a systematic review. J Affect Disord, 318:364-379.