Gaussian Classifier System for Accurate Lung Cancer Prediction and Improved Patient Outcomes

One of the most important and pressing needs in the machine learning field is medicine. Because medical field data is inherently complicated and diagnosis needs analysis of many different kinds of data, these criteria are of the utmost importance. There is a great deal of danger associated with cancer detection, making it an area that demands absolute accuracy and dependability. It is now essential to have quicker and more precise detections since they greatly increase the amount of time patients may live. Many different kinds of cancer may develop in different parts of the body. To that end, there is a great deal of variation across cancer types in terms of this characteristic. On top of that, there shouldn't be a unified method for cancer detection since various types of cancer have varied needs. The creation of models for the detection of lung cancer is the centre of this thesis. When it comes to cancers, lung cancer is among the most important since it kills the most people. The detecting method is very complicated because of all the many factors that need to be considered. The three primary findings from this thesis tackle the problems with lung cancer forecasting in a way that is both effective and efficient. Improvement over the prior model is achieved by the development of a model that can increase the levels of predictability. In order to deal with the intricacy of data, a more involved technique is used. The suggested approach Gaussian Classifier System (GCS) surpasses the other two methods, Naïve Bayes (NB) and Logistic Regression (LR), when compared side by side. Naïve Bayes performs well, however Logistic Regression outperforms it with enhanced prediction skills. The suggested technique, on the other hand, outperforms both of them with a 93% accuracy rate, demonstrating that it successfully balances precision and recall while keeping a high degree of accuracy.