Lung Cancer Detection using Python and Machine Learning

Lung cancer is a leading cause of mortality worldwide, and improving survival rates depends heavily on early detection. Machine learning offers an effective approach for analyzing medical data to support timely diagnosis. This study evaluates the classification of lung cancer cases using medical features such as imaging results, symptoms like features, including demographic data (age, gender), environmental and lifestyle factors (air pollution, alcohol use, dust allergy, occupational hazards, genetic risk, smoking, passive smoking, balanced diet, obesity), chronic lung disease status, and various clinical symptoms (chest pain, coughing blood, fatigue, weight loss, shortness of breath, wheezing, swallowing difficulty, clubbing of finger nails, frequent cold, dry cough). All features are complete with no missing values. The models were trained and tested on a publicly available lung cancer dataset. Results indicate in diagram and various from we use the python for code and give result