Lung Cancer Disease Prediction Model with Multiple Linear Regression
Abstract
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, particularly in developing countries where smoking prevalence is high. This study aims to develop a predictive model for lung cancer risk using multiple linear regression based on two main factors: genetic predisposition and exposure to passive smoking. The research was conducted using an observational analytic design with secondary data derived from cancer registries, hospital medical records, and national health surveys. Data processing included cleaning, imputation of missing values, and standardization of exposure variables. The results of the regression analysis showed that both genetic risk and passive smoking significantly increased the lung cancer risk score, with coefficients of 0.24 and 0.48, respectively. Interestingly, passive smoking demonstrated a stronger impact compared to genetic predisposition, indicating its role as a more dominant determinant of lung cancer risk. The model explained 20.5% of the variation in risk, while the remaining was influenced by other factors such as air pollution, occupational exposure, and lifestyle. These findings highlight the importance of strengthening public health policies, particularly tobacco control in public spaces, and implementing targeted risk-based screening strategies. This predictive model offers a practical tool for early detection, efficient allocation of health resources, and effective cancer prevention strategies.
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