Comparison of Supervised Learning Algorithms for Cancer Prediction
Keywords:
Cancer Prediction, Decision Tree, Machine Learning, Naive Bayes, Support Vector MachineAbstract
This study focuses on the application of Machine Learning algorithms for cancer prediction using a classification dataset. Several algorithms were employed, including K-Nearest Neighbor (KNN), Naive Bayes Classifier, Decision Tree, Random Forest, and Support Vector Machine (SVM). The primary goal of this research is to evaluate the performance of each algorithm to identify the best method for achieving high accuracy in cancer classification prediction. The experimental results reveal variations in performance among these algorithms. The evaluation was conducted using metrics such as accuracy, precision, recall, and F1-Score. Based on the analysis, Random Forest and Support Vector Machine demonstrated the best performance with the highest accuracy compared to other algorithms. Meanwhile, the Naive Bayes algorithm tended to exhibit lower performance in predictions. This study emphasizes the importance of selecting the appropriate algorithm in the implementation of Machine Learning for medical applications such as cancer prediction. With these findings, it is hoped that the identified methods can assist in clinical decision-making and improve the accuracy of early cancer diagnosis.
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