Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare
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J. Balajee
Abstract
Machine learning (ML) has emerged as a transformative force in healthcare, driving a paradigm shift toward data-driven decision-making. This chapter explores use cases and practical examples of how ML is revolutionizing clinical and operational aspects of healthcare systems. From predictive analytics and patient risk stratification to diagnostic support, personalized treatment, and administrative optimization, ML is enabling faster, more accurate, and more efficient decisions. With the ever-growing volume of digital health data, medical imaging, and real-time sensor information, healthcare providers are increasingly relying on ML models to uncover actionable insights. However, successful implementation hinges on understanding domain-specific challenges, including data quality, interpretability, privacy concerns, and regulatory compliance. Through real-world case studies and application-driven dialog, this chapter aims to bridge the gap between theoretical potential and practical deployment. We also highlight recent breakthroughs and discuss strategies for integrating ML models into clinical workflows. The ultimate goal is to guide machine learning engineers, data scientists, and healthcare professionals in applying ML to deliver better patient outcomes, reduce costs, and enhance system efficiency.
Abstract
Machine learning (ML) has emerged as a transformative force in healthcare, driving a paradigm shift toward data-driven decision-making. This chapter explores use cases and practical examples of how ML is revolutionizing clinical and operational aspects of healthcare systems. From predictive analytics and patient risk stratification to diagnostic support, personalized treatment, and administrative optimization, ML is enabling faster, more accurate, and more efficient decisions. With the ever-growing volume of digital health data, medical imaging, and real-time sensor information, healthcare providers are increasingly relying on ML models to uncover actionable insights. However, successful implementation hinges on understanding domain-specific challenges, including data quality, interpretability, privacy concerns, and regulatory compliance. Through real-world case studies and application-driven dialog, this chapter aims to bridge the gap between theoretical potential and practical deployment. We also highlight recent breakthroughs and discuss strategies for integrating ML models into clinical workflows. The ultimate goal is to guide machine learning engineers, data scientists, and healthcare professionals in applying ML to deliver better patient outcomes, reduce costs, and enhance system efficiency.
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
