Abstract: Lung cancer is a kind of cancer that originates in the lungs and cannot be prevented in its late stages of development, but its risk can be reduced using the sessions. As a result, quick detection of lung cancer can help to lower the survival rate. The number of chain smokers is probably equal to the number of people affected by lung cancer. The lung cancer is predicted using the Logistic Regression. The study employs logistic regression to analyse categorical datasets. After evaluating parameters and assessing the significance of each influencing attribute, the model undergoes testing. This process yields 18 prediction models and identifies factors correlating with disease size risk. Utilizing logistic regression, the study predicts lung cancer occurrence in patients based on various factors such as symptoms, habits, and health history. Notable symptoms associated with lung cancer include smoking, alcohol consumption, swallowing difficulties, coughing, chronic ailments, fatigue, and age.

Keywords: Prediction, Logistic Regression, Machine Learning.

| DOI: 10.17148/IARJSET.2024.115112