A Comprehensive Analysis of Ovarian cancer utilizing SVMs and Artificial Neural Networks

Accurate and timely cancer diagnosis is crucial for effective treatment and patient survival. This research investigates the application of machine learning algorithms, specifically Support Vector Machines (SVM) and Artificial Neural Networks (ANN), to improve cancer diagnosis. The study utilizes a dataset containing both malignant and benign tumor samples to train and evaluate the proposed models. The performance of the models is assessed using metrics such as accuracy, sensitivity, specificity, and AUC. The results demonstrate that SVM outperforms ANN in classifying cancer types, achieving a higher accuracy of $\mathbf{9 8. 0 8 \%}$ compared to $\mathbf{8 9 \%}$ for ANN. This research highlights the potential of machine learning in enhancing cancer diagnosis and improving patient outcomes.