Open Access

ARTICLE

Machine Learning Models for Predicting Smoking-Related Health Decline and Disease Risk

Vaskar Chakma^1,*, Md Jaheid Hasan Nerab¹, Abdur Rouf¹, Abu Sayed², Hossem Md Saim³, Md. Nournabi Khan³

1 School of Artificial Intelligence and Computer Science, Nantong University, Nantong, 226019, China
2 School of Transportation and Civil Engineering, Nantong University, Nantong, 226019, China
3 School of Mechanical Engineering, Nantong University, Nantong, 226019, China

* Corresponding Author: Vaskar Chakma. Email:

Journal of Intelligent Medicine and Healthcare 2026, 4, 1-35. https://doi.org/10.32604/jimh.2026.074347

Received 09 October 2025; Accepted 12 November 2025; Issue published 23 January 2026

Abstract

Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators including body measurements, blood tests, and demographic information. We tested three advanced prediction algorithms—Random Forest, XGBoost, and LightGBM—to determine which could most accurately identify people at high risk. This study employed a cross-sectional design to classify current smoking status based on health screening biomarkers, not to predict future disease development. Our Random Forest model performed best, achieving an Area Under the Curve (AUC) of 0.926, meaning it could reliably distinguish between high-risk and lower-risk individuals. Using SHAP (SHapley Additive exPlanations) analysis to understand what the model was detecting, we found that key health markers played crucial roles in prediction: blood pressure levels, triglyceride concentrations, liver enzyme readings, and kidney function indicators (serum creatinine) were the strongest signals of declining health in smokers. These results demonstrate that artificial intelligence can serve as a powerful tool for early disease detection in smokers. By identifying at-risk individuals before conventional symptoms appear, healthcare providers could intervene earlier with personalized prevention strategies. Implementing these predictive systems in public health programs could reduce the enormous burden smoking places on healthcare systems while shifting medical care from reactive treatment to proactive prevention.

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Keywords

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