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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Alsafi, Fatima
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Computer Science & IT Engineering

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Machine learning for mental health: predicting transitions from addiction to illness Alkhazraji, Ali; Alsafi, Fatima; Dbouk, Mohamed; Ibrahim, Zein Al Abidin; Sbeity, Ihab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp385-396

Abstract

The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. 
Co-Authors Alkhazraji, Ali Dbouk, Mohamed Ibrahim, Zein Al Abidin Sbeity, Ihab