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

TL;DR

This study evaluates machine learning models, especially Random Forest, for identifying high-risk smoking-related health decline using clinical data, achieving high accuracy and interpretability with key health markers.

Contribution

It systematically compares ML algorithms for smoking risk assessment, emphasizing interpretability and practical deployment over algorithmic novelty.

Findings

Random Forest achieved an AUC of 0.926 in risk classification.

Key health indicators like blood pressure and liver enzymes are crucial for prediction.

SHAP analysis identified the most influential health markers for smoking-related risk.

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.