Machine Learning Techniques for Predicting SRHD: Smoking-Related Health Decline

Purpose: Leveraging machine learning techniques allows for a deeper understanding and prediction of health issues related to smoking. Identifying specific health markers enables the early detection of potential problems in smokers, facilitating timely interventions.Methods: We analyzed a vast health dataset that included 55,691 records from individuals. Each record contained various health indicators such as blood pressure, cholesterol levels, liver enzymes, and kidney function markers. We focused on the smoking status of participants (whether they weresmokers or non-smokers) and used different machine learning models to predict health risks based on these indicators.Results: Our findings showed that the Random Forest model performed the best, achieving an impressive score of 0.907 in distinguishing between health risks for smokers and non-smokers. This model was particularly good at identifying how smoking impacts different health areas, including the heart, liver, and kidneys.Conclusion: This research demonstrates that machine learning tools can be valuable in predicting health issues related to smoking. By combining effective prediction with easy-to-understand insights, these models can help healthcare providers identify at-risk smokers sooner and tailor interventions to improve their health. Our work highlights the potential for personalized approaches to assist smokers in managing their health better.