Abstract:
Physical illnesses like lung cancer have become more common nowadays. The world
nowadays is aware of the subject. The disease known as lung cancer affects most
individuals. A measure of the sickness is the variations in diagnostic report ratios between
patients who are normal and those who are afflicted. Numerous investigations have already
been conducted on the illness of lung cancer. I've identified a few excellent chances to
improve the procedure even further. I suggest using effective algorithm models to
anticipate hazards and raise early alert. My suggested approach is easy to utilize in practice
and appropriate for basic projections of lung cancer sickness. The GitHub website served
as the dataset's host. Several five distinct kinds of algorithms, including Decision Trees,
KNN, ANN, SVM, and LR (logistic regression), have been used. In order to defend the
performances, I additionally used a few other ensemble models. ANN had the accuracy at
99.50%, KNN at 99.50%, and SVM at around 97.50%. After that, I received a perfect score
of 100% in both Decision Tree and Logistic Regression. I optimized each classifier's
parameters using hyper-parameter tweaking. The experimental research analyzed the
findings of other recent studies and produced more accurate estimates of lung cancer
disease, with 100% accuracy being the greatest performance
