A Survey of Classification Algorithms in Supervised Machine Learning

Machine learning is crucial in enhancing predictive and diagnostic capabilities across multiple sectors. Professionals can use it to identify potential conditions and assess the risks associated with different intervention strategies. Machine Learning methods have shown significant potential in enhancing disease detection by offering accurate, efficient, and automated diagnostic capabilities. Supervised machine learning is a widely used approach in artificial intelligence that enables systems to learn from labeled data and make accurate predictions. This paper explores various supervised learning techniques, including classification models, which are applied across diverse domains such as healthcare, finance, and natural language processing. This study focuses on the approaches and the applications of supervised learning and highlights its benefits, and discusses ongoing challenges and future directions for improving machine learning-based healthcare solutions.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-28
DOI
https://doi.org/10.5281/zenodo.17753525
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

A Survey of Classification Algorithms in Supervised Machine Learning

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare
article

A Survey of Classification Algorithms in Supervised Machine Learning

article en

Abstract

Machine learning is crucial in enhancing predictive and diagnostic capabilities across multiple sectors. Professionals can use it to identify potential conditions and assess the risks associated with different intervention strategies. Machine Learning methods have shown significant potential in enhancing disease detection by offering accurate, efficient, and automated diagnostic capabilities. Supervised machine learning is a widely used approach in artificial intelligence that enables systems to learn from labeled data and make accurate predictions. This paper explores various supervised learning techniques, including classification models, which are applied across diverse domains such as healthcare, finance, and natural language processing. This study focuses on the approaches and the applications of supervised learning and highlights its benefits, and discusses ongoing challenges and future directions for improving machine learning-based healthcare solutions.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 36%
Artificial Intelligence in Healthcare
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