Classifying Lung Cancer Disease Using Random Forest Algorithm

Through the use of a Random Forest Classifier and the utilization of a varied dataset, this research presents a machine learning model that is targeted at the early diagnosis of lung cancer. An outstanding overall accuracy of 97% is shown by the model, which demonstrates its capacity to properly identify between those who have lung cancer and those who do not have lung cancer. Underscoring the model's skill in limiting false positives and assuring exact identification of positive cases, precision measures suggest a significant accuracy of 0.98 for Class 1 (Lung Cancer). This demonstrates the model's ability to effectively identify positive instances. To provide a complete approach, the model takes into account important characteristics such as age, smoking status, and anxiety levels. The high recall of 0.99 for Class 1 indicates that the model is efficient in catching the majority of lung cancer cases, which is essential for administering timely therapies. This study contributes to the current improvements in healthcare by offering a viable tool for the early diagnosis of lung cancer and perhaps improving the results for patients.