Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing - Towards Data-Driven Decision-Making and Precise Medicine

Aug 4, 2026

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Artificial intelligence is reshaping healthcare data workflows, accelerating the collection, management, processing, and analysis of information from electronic medical records, physiological signals, and medical images. This editorial introduces a Special Issue compiling research and opinion articles on applying machine learning and deep learning to diverse healthcare data. Highlighted studies cover Alzheimer’s prediction, clinical outcome modeling for cerebral aneurysms, Parkinson’s severity classification from video and gait representations, gastric cancer detection via ensembles, and AI-based quantitative lung texture analysis for bronchiectasis insights.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages6

Reading Time15 min

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Editorial Overview

AI and Healthcare Data Foundations

Machine Learning Applications

Deep Learning Applications

Quantitative Feature Analysis in Diagnostics

Editorial Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing: Towards Data-Driven Decision-Making and Precise Medicine Haipeng Liu 1, * and Rajesh Kumar Tripathy 2, * Received: 10 April 2025 Accepted: 17 April 2025 Published: 21 April 2025 Citation: Liu, H.; Tripathy, R.K. Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing: Towards Data-Driven Decision-Making and Precise Medicine. Diagnostics 2025, 15, 1051 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics15081051 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK 2 Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad 500078, India * [Correspondence: haipeng.liu@coventry.ac.uk](Correspondence: haipeng.liu@coventry.ac.uk) (H.L.); [tripathyrk@hyderabad.bits-pilani.ac.in](tripathyrk@hyderabad.bits-pilani.ac.in) (R.K.T.) Artificial intelligence (AI) is reshaping the landscape of healthcare data. Alongside electronic medical records (EHRs), AI algorithms are accelerating the collection, management, processing, and analysis of healthcare data [1] . Its power was recently showcased through the realization of AI-based methods for the early detection and fine-grained severity evaluation of COVID-19 . Machine learning and deep learning models enable the automatic processing of multimodal healthcare data, including EHRs, physiological signals, and medical images. In this Special Issue,“Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing”, we collect eleven research and opinion articles focusing on the application of AI models to different types of healthcare data. The published studies in this Special Issue provide up-to-date examples of AI uses in a healthcare context (Table 1) . Machine learning can comprehensively analyze different features and estimate the risk, enhancing diagnoses and prognoses. Based on a longitudinal dataset of 150 people, Alshamlan et al. compared some common machine learning models, including support vector machine (SVM), random forest (RF), and logistic regression (LR) approaches, in predicting Alzheimer’s disease. Minimum redundancy maximum relevance (mRMR) and mutual information (MI) were employed for feature selection, and LR combined with mRMR achieved the highest accuracy of 99.08% in predicting Alzheimer’s disease. Their results highlighted the role of AI in disease prediction and clinical decision-making. Toader et al. used machine learning models to predict clinical outcomes in microsurgical clipping treatments of cerebral aneurysms, based on a dataset of 344 patients’ preoperative characteristics. Validating prediction outcomes on the Glasgow Outcome Scale (GOS), their extreme gradient boosting (XGB) model outperformed the others and achieved an area under the receiver operating characteristic curve (AUC ROC) of 0.72 ± 0.03 for specific GOS outcome prediction, and an AUC ROC of 0.78 ± 0.02 for the binary classification of outcomes. These results demonstrate the potential of machine learning as a tool for predicting the surgical outcomes of ruptured cerebral aneurysm treatments. Moreover, the study underscores the need for high-quality, large-scale datasets and external validation in order to enhance the reliability and generalizability of machine learning models. Deep learning enables the in-depth analysis of clinical images, making automatic classification and fine-grained feature analysis possible. In their contribution, Hadj-Alouane et al. propose an AI framework for the diagnosis and severity

FAQs

What healthcare data sources are discussed as suitable for AI-based processing?

The text highlights electronic medical records (EHRs), physiological signals, and medical images as key multimodal data sources for AI processing and analysis.

How does the editorial frame the goal of using machine learning and deep learning in healthcare?

It emphasizes automatic processing for risk estimation, disease prediction, clinical outcome modeling, and fine-grained severity evaluation to support data-driven decision-making and more precise medicine.

What does the editorial say about dataset quality and validation for clinical AI models?

It notes the need for high-quality, large-scale datasets and external validation to improve the reliability and generalizability of machine learning models.

Aug 4, 2026

Upload by Rowan