Fundamentals of Machine Learning and Deep Learning for Healthcare Applications

Swapna Katta (), Prabhishek Singh (), Deepak Garg () and Manoj Diwakar ()
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Swapna Katta: SR University
Prabhishek Singh: Bennett University
Deepak Garg: SR University
Manoj Diwakar: Graphic Era Deemed to be University

A chapter in Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care, 2025, pp 267-279 from Springer

Abstract: Abstract In the last few years, there has been widespread application of Machine Learning (ML)/Deep learning (DL) techniques, which have demonstrated superior performance in various domains such as image processing and Natural language processing (NLP), especially in the healthcare system. ML algorithms include effective models for performing efficient data analysis to uncover complex patterns and meaningful information from vast data sets, enabling predictive analytics to make discoveries in reasonable time. ML algorithms have been utilized in healthcare applications such as genomic information analysis, medical records, laboratory results, detecting abnormalities in medical images, optimizing patient care plans, and predicting disease outcomes. Especially, Deep learning techniques, in particular, have shown promising outcomes in pattern recognition, medical image analysis, and disease diagnosis using Multi-layered Neural network in healthcare systems. Given the vast amount of medical data, the need for customized medicine, and real time decision making has driven the adoption of ML/DL in medical services. This chapter provides a comprehensive overview of ML/DL applications in healthcare, their evolution in medical field, significance, and future directions. It also explores challenges such as data security and model interpretability in healthcare sector. Human healthcare professionals and researchers can enhance ML/DL applications in medical field to improve accuracy in medical diagnosis, patient care, and medical research.

Keywords: Machine learning; Deep learning; Healthcare applications; Significance; Future trends (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-98728-1_13

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