Intelligent Prediction of Cancer Diseases through Machine Learning

Abstract

Cancer remains one of the leading causes of mortality worldwide, and timely diagnosis plays a critical role in improving patient survival rates. Traditional diagnostic methods often face challenges such as complexity, cost, and human error, necessitating the development of intelligent computational systems. This study proposes a machine learning–based framework for the intelligent prediction of cancer diseases, aiming to improve accuracy, reduce misdiagnosis, and support clinical decision-making. The proposed approach integrates feature selection, optimized model training, and performance evaluation to construct a scalable predictive model applicable to various types of cancer.

Keywords: Cancer Prediction, Machine Learning, Intelligent Systems, Early Diagnosis, Clinical Decision Support