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Improving Accuracy: Comparative Analysis of Machine Learning Models for Prostate Cancer Prediction

Year: 2023

Type: article

Abstract: Among the different types of cancer affecting men is prostate cancer, which ranks second in mortality after lung cancer, a worrying reality. Nowadays, Machine Learning (ML) models have contributed to different areas, being their contribution to the medical field one of the most outstanding. This study aims to compare the accuracy of ML models in the prediction of prostate cancer. Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT) and Adaptive Boosting (AdaBoost) models were analyzed. In addition, DT, RF, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB) and Logistic regression (LR) models were used to identify the base model for algorithm optimization. The study was divided into several stages, such as the description of the models and the analysis of the data set, among others. On the other hand, the metrics of sensitivity, precision, specificity, accuracy, and F1 count were used to contrast the algorithms. The training results positioned the GB algorithm as the most accurate algorithm for prostate cancer detection with 83.33% accuracy, 98.02% precision and 95.24% sensitivity.

Source: International Journal of Intelligent Systems and Applications in Engineering

Authors: Clemente, Saul Beltozar-, Vega, Enrique Diaz-, Ramos, Isaac Conde, Navarrete, Raul Tejeda

Institutions: Universidad Científica del Sur, Private University of the North, Universidad Tecnológica del Perú

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W7216711027

FWCI:

Cites: 37

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Related to: 10