Analysis of Naive Bayes Algorithm for Lung Cancer Risk Prediction Based on Lifestyle Factors - Vabilla, Sheila Anggun; Rahardi, Majid (Politeknik Negeri Batam, 2025)
Lung cancer is one of the types of cancer with the highest mortality rate in the world, which is often difficult to detect in the early stages due to minimal symptoms. This study aims to build a lung cancer risk prediction model based on lifestyle factors using the Gaussian Naive Bayes algorithm. Data fit is addressed using the Synthetic Minority Over-sampling Technique (SMOTE), and feature selection is carried out using the Mutual Information. The dataset used consists of 1000 patient data with 24 features related to lifestyle and environmental factors. Model validation is carried out using 5-fold Stratified Cross Validation, and evaluated based on accuracy, precision, recall, and confusion matrices. The results show that the application of SMOTE successfully increases the model accuracy to 91.00% with high precision and recall values in all risk classes (Low, Medium, High). The features