Clinical data; Cyberknife; Data mining; Machine Learning

This chapter delves into the integration of data mining and machine learning techniques to enhance CyberKnife radiotherapy, focusing on personalizing treatment plans and improving clinical decision-making. The study analyzes a dataset of 61 patients, exploring relationships between clinical parameters, diagnoses, and treatment outcomes. Key findings include strong associations between age, sex, and diagnosis duration, with male patients showing a higher likelihood of complications. The analysis employs logistic regression and Random Forest models to predict complications and hospitalization duration, highlighting the dominant role of age in predicting outcomes. The chapter also discusses the challenges and future directions in leveraging data mining for personalized medicine, emphasizing the need for robust cybersecurity measures and seamless integration into healthcare frameworks. By refining these analytical approaches, the study aims to improve patient care and optimize treatment strategies in clinical settings.

This chapter delves into the integration of data mining and machine learning techniques to enhance CyberKnife radiotherapy, focusing on personalizing treatment plans and improving clinical decision-making. The study analyzes a dataset of 61 patients, exploring relationships between clinical parameters, diagnoses, and treatment outcomes. Key findings include strong associations between age, sex, and diagnosis duration, with male patients showing a higher likelihood of complications. The analysis employs logistic regression and Random Forest models to predict complications and hospitalization duration, highlighting the dominant role of age in predicting outcomes. The chapter also discusses the challenges and future directions in leveraging data mining for personalized medicine, emphasizing the need for robust cybersecurity measures and seamless integration into healthcare frameworks. By refining these analytical approaches, the study aims to improve patient care and optimize treatment strategies in clinical settings.