Comparison of Machine Learning Algorithm Performance on Healthcare and Technology Datasets
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Abstrak
Machine Learning has become a powerful tool across different domains, including healthcare and consumer technology. This study compares the performance of classical Machine Learning algorithms—Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree (DT)—using two contrasting datasets: the Mobile Price Classification dataset and the Lung Cancer Survey dataset. Both datasets undergo identical preprocessing methods including normalization, label encoding, and dataset splitting (80:20 and 90:10). Evaluation metrics include accuracy, precision, recall, F1-score, and ROC-AUC. The results show that SVM provides the most consistent performance across both domains, while Decision Tree performs exceptionally well on the medical dataset but poorly on the technology dataset due to overfitting. This cross-domain experiment highlights how dataset characteristics significantly influence algorithm performance and provides insights for selecting appropriate algorithms based on domain-specific requirements.
Keywords: Classification, Lung Cancer, Machine Learning, Mobile Price, SVM,
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