Machine Learning in Healthcare: Data-Driven Decisions, Predictive Modelling, Personalized MedicineSaurav Mallik, Sandeep Kumar Mathivanan, S.K.B Sangeetha, Ben Othman Soufiene, Saravanan Srinivasan, Aimin Li This book explores the applications of machine learning techniques in the healthcare industry to optimize decision-making processes. The book delves into the ways in which machine learning can be used to analyze large and complex healthcare data sets, such as electronic health records, medical imaging, and wearable device data, to extract valuable insights and improve patient care.
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Contents
| 1 | |
| 21 | |
| 41 | |
A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors | 61 |
DataDriven Machine Learning Strategies for Oncological Disease Prediction and EarlyStage Detection | 83 |
EnsembleBased Predictive Modeling for Depression and Anxiety detection | 103 |
Using Federated Learning to Protect Patient Data | 129 |
A Spatiotemporal Hybrid Learning Framework for RealTime Epidemic Forecasting | 149 |
Hybrid AttentionDriven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective | 271 |
A Multiscale AttentionGuided 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 EvidenceBased Data Utilization | 335 |
An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy | 367 |
A Hierarchical CrossFusion 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 |
Practical Applications of Machine Learning for DataDriven DecisionMaking in Healthcare | 431 |
A Case Study in Diabetes Prediction | 171 |
Machine Learning Techniques for Healthcare | 193 |
Applications and Benefits of Machine Learning in Healthcare | 215 |
A PatientCentered Approach to Digital Health Transformation | 233 |
ReinforcementDriven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems | 251 |
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accuracy AdaBoost analysis applications approach architecture artificial intelligence base learners big data blockchain cancer cervical cervical cancer challenges classification clinicians complex Computer dataset decision trees decision-making deep learning diabetes diagnosis digital health disease e-mail early efficiency EHRs electronic health records enhance ensemble model ensure EpiCastNet evaluation F1-score feature extraction federated learning Figure framework global gradient boosting health data healthcare healthcare data healthcare systems heart rate heterogeneous hybrid model IEEE improve integration interpretability logistic regression LSTM machine learning medical image medicine mental health methods metrics ML algorithms ML models monitoring multi-objective multi-scale multimodal neural networks nodes optimization outcomes output overfitting patient data patterns physiological precision predictive analytics predictive models preprocessing privacy-preserving proposed random forest real-time real-world reinforcement learning risk robust scalable sensor SHAP spatial stress detection stroke techniques tion TNPE transformation treatment updates utilized validation vector XGBoost