Prediction of Breast Cancer utilizing Logistic Regression, Decision Tree, Random Forest

Breast Cancer is the first cause of death for cancer in women. The application of Machine learning techniques in medical fields is one of the fastest-growing areas in disease diagnosis. A machine learning-based breast cancer prediction system is designed for early diagnosis. It shows the traditional methods, which are time-consuming. The following study develops a system for diagnosing the prediction of Breast Cancer that will help the doctors to predict the disease of the patients. This paper presents a dataset containing 569 samples in total, where records are labeled as either benign or malignant. The machine learning models for Predicting Breast Cancer implement and test datasets. Thus, we compared the precision, F1, recall, and accuracy of all three ML algorithms, and the accuracy of the Decision Tree, Logistic Regression, and Random Forest model is 96%, 93%, and 97%, respectively. Thus, better accuracy from the Random Forest model is 97% which is best out of all three models. In future this model can be used for tier-three cities for the early detection of breast cancer.