Predicting Cancer Outcomes: A Comparative Study of ML Models

Prognostic accuracy in cancer is vital for timely diagnosis and effective treatment planning. This study evaluates the performance of three machine learning techniques—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT)—in forecasting cancer progression using clinical and histopathological data. Results demonstrate that SVM surpasses KNN and DT in predictive precision, establishing its robustness in prognostic modeling. The research highlights how machine learning can support clinicians with data-driven decision-making tools to improve patient care. Future directions may involve advanced deep learning models and optimized feature selection to enhance predictive capabilities further.