LUNG CANCER DISEASE PREDICTION AND CLASSIFICATION BASED ON FEATURE SELECTION METHOD USING BAYESIAN NETWORK, LOGISTIC REGRESSION, J48, RANDOM FOREST, AND NAÏVE BAYES ALGORITHMS

Viji Cripsy JDivya T2023-08-182023-08-182023-03-31IEEE Xplorehttps://ieeexplore.ieee.org/document/10127881People who have never smoked can get lung cancer, but smokers have a higher risk than non-smokers. Any aspect of the respiratory system can be affected by lung cancer, which can start anywhere in the lungs, Different classification methods are used for lung cancer prediction. This article uses five different classification algorithms to predict lung cancer in patients using Kaggle dataset. Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes methods are used, Based on the carefully identified correct and incorrect cases, the quality of the result was measured using the evaluation technique and the WEKA tool. The experimental results showed that Logistic Regression performed best (91.90 % ), followed by Naive Bayes (90.29 % ), Bayesian Network (88.34 % ), j48 (86.08 % ) and Random Forest (90.93 % ).en-USRadio frequencyLung cancerLungMedical servicesForestryPrediction algorithmsFeature extractionLUNG CANCER DISEASE PREDICTION AND CLASSIFICATION BASED ON FEATURE SELECTION METHOD USING BAYESIAN NETWORK, LOGISTIC REGRESSION, J48, RANDOM FOREST, AND NAÏVE BAYES ALGORITHMSArticle