Lung Cancer Detection, Prediction and Analysis of Lifestyle Parameters using ML and AI Techniques
Keywords:
Artificial Intelligence, Lifestyle, Lung Cancer, Machine Learning, PredictionAbstract
Cancer poses a significant threat to human life, often diagnosed in later stages, highlighting the crucial need for early prediction. Literature extensively explores Machine Learning, Data Mining, and Artificial Intelligence techniques for the identification, classification, prediction, and detection of various cancers like lung, breast, prostate, skin, liver, and recurrence cancer. Predictive models rely on vast datasets for cancer prediction. Lung cancer's development is closely tied to lifestyle factors such as smoking, air pollution, and diet imbalance, emphasizing the potential of lifestyle indicators in early detection. A study focuses on constructing a model using lifestyle data to predict lung cancer and categorize its severity. Basic lifestyle parameters are initially examined, and if lung cancer potential is indicated, a second component of the model further analyzes each parameter to predict cancer level. Various Machine Learning techniques including Support Vector Machine, Logistic Regression, and Linear regression are applied to predict lung cancer risk and level with analysis of prediction using AI techniques. SVM emerges as the most effective classifier for predicting lung cancer risk based on lifestyle factors, while linear regression is optimal for total risk score prediction. Additionally, gender- and age-specific lifestyle parameters contributing to lung cancer are identified. The study's preliminary phase employs logistic regression and Support Vector Machine to predict lung cancer, achieving high accuracies of 94% and 90% respectively. The subsequent component utilizes SVM, Random Forest, KNN and Linear Regression to estimate cancer malignancy levels, with accuracies reaching 98% , 96 and 97% respectively. The study aims to predict lung cancer early using lifestyle data, offering insights into risk factors and preventive strategies.
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