Tumor Detection Using ML

Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection and accurate diagnosis play a crucial role in improving survival rates and enabling timely medical intervention. Traditional diagnostic methods often rely on manual examination and expert interpretation, which can be time-consuming and susceptible to human error. The rapid advancement of Machine Learning (ML) technologies has created new opportunities for developing intelligent systems capable of supporting healthcare professionals in disease diagnosis and clinical decision-making. This paper presents the implementation and deployment of a Web-Based Breast Tumor Detection System that utilizes the Random Forest machine learning algorithm to classify tumors as either benign or malignant. The proposed system is developed using the Wisconsin Breast Cancer Diagnostic Dataset, which contains various tumor-related clinical features such as radius, texture, perimeter, area, smoothness, and symmetry. The collected data undergoes preprocessing steps including feature validation, normalization, and label encoding to improve prediction performance and model reliability.