Veeramuthu P

https://doi.org/10.64823/ijter.2505009

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.