Citation :

Atta Yaw Agyeman, Samuel Gbli Tetteh, 2024. "Technical Evaluation of Machine Learning Models: An Empirical Study" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 1: 1-9.

Abstract :

In the current era of technological advancement, the proliferation of diverse data sources has revolutionised decision-making processes across the globe. This exponential growth in data availability has reshaped decision-making paradigms and unlocked unprecedented opportunities for applying machine learning methodologies. Mainly, domains such as disease detection and intricate economic analysis have witnessed a significant transformation due to the advent of machine learning algorithms. Amidst these developments, the incidence of breast cancer continues to surge in both developed and developing nations, posing significant challenges to healthcare systems worldwide. In response to this pressing concern, this study endeavours to amalgamate these trends by comprehensively analysing major machine learning models to classify breast cancer tissues. Utilising the Wisconsin Breast Cancer Dataset as the foundational framework, this research aims to evaluate the efficacy of various machine learning algorithms in distinguishing between benign and malignant tissues. The repertoire of machine learning models under scrutiny encompasses Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbors (KNN), as well as two variants of Support Vector Machine (SVM) — Radial Basis Function (RBF) and Linear classifier. Additionally, the study incorporates Decision Tree Classifier and Random Forest (RF) algorithms into its comparative analysis. The study's findings underscore the pivotal role of Random Forest (RF) and the diverse variations of Support Vector Machine (SVM) in achieving remarkable classification accuracy. Moreover, these models exhibit superior precision, recall, and f1-score performance metrics, highlighting their efficacy in breast cancer tissue classification tasks.

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Keywords :

Support Vector Machine (SVM), Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree Classifier, Random Forest (RF).