Predicting quality of life based on mental health state - A machine learning approach using Urban-HEART 2

Aug 4, 2026

Upload by Ezra

Quality of life (QoL) serves as a key public-policy indicator, yet its relationship with mental health remains complex. This study predicts QoL from mental health state using Urban-HEART 2 survey data collected in Tehran, Iran. A secondary analysis of 117,839 participants applies supervised machine-learning models (Random Forest, Decision Tree, SVM, Naive Bayes, Logistic Regression) and unsupervised k-means clustering to segment mental-health profiles and forecast QoL labels with high accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Quality of life and public policy

Mental health burden and QoL in services

Methods

Data source: Urban-HEART 2

Machine learning and clustering approach

Results

Clustering mental-health features into clusters

QoL prediction performance by model

Conclusions

Mental health features and QoL label prediction

Take-home message

Original Article in Public Health Predicting quality of life based on mental health state: A machine learning approach using Urban-HEART 2 Hamid SADEGHI1, Masoud GHARIB2, Vahidreza BORHANINEJAD3, Vahid RASHEDI4* 1Department of Electrical and Computer Engineering, Wayne State University, Michigan, Detroit, USA. Email: [h.sadeghi.1991@gmail.com](h.sadeghi.1991@gmail.com) 2Orthopedic Research Center, Department of Rehabilitation Sciences, School of Allied Medical Sciences, Mazandaran University of Medical Sciences, Sari, [Iran. Email: gharib_masoud@yahoo.com. ORCID: 0000-0002-6368-9736](Iran. Email: gharib_masoud@yahoo.com. ORCID: 0000-0002-6368-9736) 3Social Determinants ofHealth Research Center, Institutefor Futures Studies in Health, Kerman University ofMedical Sciences, Kerman, Iran. Email: [borhani777@yahoo.com. ORCID: 0000-0002-4689-6741](borhani777@yahoo.com. ORCID: 0000-0002-4689-6741) 4Iranian Research Center on Aging, Department of Aging, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. Email: [vahidrashedi@yahoo.com. ORCID: 0000-0002-3972-3789](vahidrashedi@yahoo.com. ORCID: 0000-0002-3972-3789) * Correspondence Cite this paper as: Sadeghi H, Gharib M, Borhaninejad V, Rashedi V. Predicting quality of life based in mental health state: A machine learning approach using Urban-HEART 2. Adv Med Psychol Public Health. 2024;1(3):133-142. Doi:10.5281/zenodo.10900463 Received: 15 January 2024 Revised: 25 February 2024 Accepted: 28 March 2024 Abstract Introduction: Quality of life (QoL) is a complex and multifaceted concept often used as an indicator in evaluating public policy. This study aimed to predict QoL based on mental health state using a machine learning approach. The analysis was conducted using data from the Urban Health Equity Assessment and Response Tool (Urban-HEART 2) survey conducted in Tehran, Iran. Methods: This secondary analysis utilized data from the second round of the Urban-HEART 2 survey, which included 117,839 participants. Various machine learning (ML) algorithms were employed, including Random Forest, Decision Tree, Support Vector Machine (SVM), Naive Bayes, and Logistic Regression. Additionally, an unsupervised learning method, specifically kmeans clustering, was used. Results: Following data preparation, the k-means clustering algorithm identified five clusters based on mental health features. ML algorithms were then utilized to predict each participant's QoL label through distinct scores. The top-performing ML algorithms based on high scores were found to be Random Forest (0.994), Decision Tree (0.991), SVM (0.990), Naive Bayes (0.935), and Logistic Regression (0.934), respectively. Conclusions: By implementing k-means clustering, we identified distinct clusters based on mental health features and assigned labels to each participant accordingly. Machine learning models accurately predicted the QoL label for each participant. All models achieved high scores (above 0.93), indicating that mental health features can reliably predict QoL labels with high accuracy. Take-home message: Metacognitive learning strategies, especially regulation, significantly impact nursing students' academic success. Integrating these strategies into curricula can enhance learning outcomes and benefit educators and students alike in nursing education. Keywords: academic performance; metacognitive learning strategies; nursing students; planning, control and regulation. INTRODUCTION Quality of life (QoL) is complex and subjective, often used to evaluate public policies and outcomes in health and social care. While the literature identifies key QoL domains applicable to adults of all ages, the importance attributed to these domains can vary among different age groups [1] . Since the 1980s, QoL has become increasingly important as a patientreported outcome in mental health services [2]. QoL theory provides a framework for conceptualizing people's mental health need

FAQs

What is the goal of the study?

The study aims to predict quality of life (QoL) based on mental health state using a machine learning approach.

What dataset and setting were used?

The analysis uses Urban-HEART 2 survey data collected in Tehran, Iran, focusing on the second round with 117,839 participants.

Which machine learning methods were evaluated?

The study used Random Forest, Decision Tree, Support Vector Machine (SVM), Naive Bayes, Logistic Regression, and an unsupervised k-means clustering method.

Aug 4, 2026

Upload by Ezra