Prediction of Lung Cancer Using Machine Learning Techniques and their Comparative Analysis

Cancer detection is done with the aid of the led expert docs and earlier tiers it may helpful. The opportunity for human error must be there. It produces the probability of error in lung cr detection which necessitates an automatic manner. Afterward, the report aims at early cancer detection through an automatic procedure to decrease human error and make the system more accurate and error-free free this system, use machine learning algorithms such as random forest, logistic regression, support vector machine and, decision tree algorithms to detect lung cancer Th research is conducted on COLAB. With COLAB or “Collaboratory” we can write and run Python in our browser, which requires a zero-configuration., free access to GPU, and is easy to share. We have implemented four algorithms on lung cancer dataset to check the performance based on diagnosis of the four parameters i.e. accuracy, Recall, Harmonic Mean, and Precision, and also presented the comparison of the four algorithms.