Özet:

Abstract Cancer was found to be a significant burdening type of problem in the medical system. An accurate diagnosis is considered to be a challenging task for physicians. Modern Artificial Intelligence (AI) research proves that cancers are easily identifiable and early detectable and can be diagnosed by classification with the help of gene mutations that occur in the cells. This paper presented a hybrid classification model using an ensemble machine learning technique to classify the type of cancer class from the given medical dataset. The proposed hybrid voting classifier utilizes guided WOA (Whale Optimization Algorithm) that aggregates different classification models' prediction results to choose the most voted class. This helps to increase the chance that individual classifiers, e.g., Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF), show significant discrepancies. The results were found to be better compared to the existing work.

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