Predictive Modeling for Cancer Diagnosis using Machine Learning

The early diagnosis of cancer significantly improves patient outcomes, yet it remains a complex challenge due to the heterogeneity of the disease. This study explores the application of machine learning (ML) algorithms to develop predictive models for cancer diagnosis. By utilizing a dataset comprising clinical and genetic data, we implement various ML techniques, including logistic regression, decision trees, support vector machines (SVM), and deep learning algorithms. Our results demonstrate the effectiveness of these models in accurately diagnosing different types of cancer, thereby highlighting the potential of ML in enhancing early detection and personalized treatment strategies. This paper provides a comprehensive analysis of the methodologies, model performance, and potential clinical implications of MLbased cancer diagnosis.