Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

TL;DR

This paper evaluates various data mining classification algorithms on the Breast Cancer Wisconsin dataset to improve early detection of malignant and benign tumors, aiming to enhance healthcare outcomes.

Contribution

It systematically compares twelve classification algorithms for breast cancer prediction using a standard dataset, highlighting their relative performances.

Findings

Decision Tree and Random Forest performed best.

Naive Bayes showed quick classification with moderate accuracy.

Multilayer Perceptron achieved high accuracy.

Abstract

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms