Comprehensive Evaluation of Machine Learning and ANN Models for Breast Cancer Detection

Abstract

Breast cancer is one of the most prevalent forms of cancer among women, and early diagnosis is of vital importance. In recent years, machine learning algorithms have demonstrated high accuracy in breast cancer detection, contributing to earlier diagnoses. Various machine learning models can analyze tumor characteristics and assist in cancer identification and treatment decisions. This project aims to comprehensively examine the performance of 14 different machine learning algorithms and a custom-developed Artificial Neural Network (ANN) model in breast cancer detection, using public Wisconsin dataset. Following data preprocessing, the training and testing stages are carried out, and the results are thoroughly analyzed to determine the model with the highest accuracy. The findings of this project will showcase the potential impact of machine learning algorithms in the clinical applications of breast cancer detection, as researchers and healthcare providers can leverage these advanced computational techniques to enhance the accuracy and efficiency of breast cancer diagnosis, ultimately leading to improved patient outcomes and the advancement of this critical field of oncology.