Prediction of Breast Cancer using Traditional and Ensemble Technique: A Machine Learning Approach

Abstract

Breast cancer is a prevalent and potentially lifethreatening disease that affects millions of individuals worldwide. Early detection plays a crucial role in improving patient outcomes and increasing the chances of survival. In recent years, machine learning (ML) techniques have gained significant attention in the field of breast cancer detection and diagnosis due to their ability to analyze large and complex datasets, extract meaningful patterns, and facilitate accurate classification. This research focuses on leveraging ML algorithms and models to enhance breast cancer detection and provide more reliable diagnostic results in the real world. Two datasets from Kaggle have been used in this study and Decision tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Classifier (KNN) etc. are applied to identify potential breast cancer cases. On the first dataset, A, the test's accuracy using Logistic Regression, SVM, and Grid SearchCV was 95.614%, however in dataset B, the accuracy of Logistic Regression and Decision Tree increased to 99.270%. The accuracy of Boosting Decision Tree was 99.270% when compared to other algorithms. To defend the performances, various ensemble models are used. To assign the optimal parameters to each classifier, a hyper-parameter tweaking method is used. The experimental study examined the findings of recent studies and discovered that LRBO performed best, with the highest level of accuracy for predicting breast cancer being 95.614%.

Optimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review

“…Meanwhile, unlabeled data use unsupervised learning to seek patterns and clusters in an unlabeled dataset. Examples of supervised learning algorithms include decision trees [41], support vector machines [42], and regression [43], whereas Principal Component Analysis (PCA) [44], [45] and K-means clustering [46] are unsupervised learning algorithms. Another ML type is reinforcement learning, in which the algorithm interacts with experience and learns to maximize the desired goal using experience, data, and trial-and-error interactions.…”