Energy Efficient Machine Learning for Sustainable Lung Cancer Prediction

Lung cancer is a principal cause of death globally and needs to be predicted early and with high accuracy. This study introduces machine learning-based systems for lung cancer prediction using Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. A dataset of 1000 instances and 25 features with prevalent risk factors such as air pollution, alcohol usage, and allergy to dust are used to train the model. In response to the limitations in cancer prediction, the work incorporates efficient data preprocessing strategies such as dealing with missing values, data resampling, and external validation. The study establishes a web interface for the risk calculation in real time by taking the health information from a user. The experimental findings provide evidence of high prediction accuracy with all algorithms, highlighting the potency of machine learning for the early detection of lung cancer. Furthermore, the study highlights the out-of-sample validation in maximizing predictive accuracy. Subsequent research will involve the integration of actual patient data to continue enhancing model stability and generalizability to real-world clinical applications. The suggested system can help healthcare professionals screen for high-risk patients, allowing for early detection and prompt intervention.