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A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors

  • R. Sunder , Bura Vijay Kumar , B. Kamala , Chilukala Mahender Reddy and S. Bathrinath
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Stroke continues to be a major cause of long-term disability globally, and precise and timely prediction of patient outcomes is needed to guide focused rehabilitation and resource allocation. This chapter introduces a new hybrid machine learning (ML) model that combines multimodal data sources for the concurrent task of risk stratification and functional outcome prediction in stroke survivors. The hybrid model combines a convolutional neural network (CNN) for imaging data, a graph neural network for structured clinical data, and a conventional machine learning model (LightGBM) for some handcrafted features. These heterogeneous elements are integrated in a common latent representation space and optimized end-to-end using a multitask learning strategy. The model was trained and validated against a robust dataset of demographic data, clinical rating, neuroimaging (CT/MRI), and follow-up functional outcomes as per the modified Rankin scale (mRS). Comparative evaluation against four traditional models: logistic regression, random forest, unimodal CNN, and an ensemble late fusion model illustrates better performance by the proposed hybrid approach with an area under the receiver operating characteristic curve of 0.92 for risk classification and a quadratic-weighted kappa of 0.84 for mRS prediction. The findings indicate that the combination of varied learning paradigms and data types in one model improves predictive power and interpretability and opens the door to more patient-centered stroke treatment.

Abstract

Stroke continues to be a major cause of long-term disability globally, and precise and timely prediction of patient outcomes is needed to guide focused rehabilitation and resource allocation. This chapter introduces a new hybrid machine learning (ML) model that combines multimodal data sources for the concurrent task of risk stratification and functional outcome prediction in stroke survivors. The hybrid model combines a convolutional neural network (CNN) for imaging data, a graph neural network for structured clinical data, and a conventional machine learning model (LightGBM) for some handcrafted features. These heterogeneous elements are integrated in a common latent representation space and optimized end-to-end using a multitask learning strategy. The model was trained and validated against a robust dataset of demographic data, clinical rating, neuroimaging (CT/MRI), and follow-up functional outcomes as per the modified Rankin scale (mRS). Comparative evaluation against four traditional models: logistic regression, random forest, unimodal CNN, and an ensemble late fusion model illustrates better performance by the proposed hybrid approach with an area under the receiver operating characteristic curve of 0.92 for risk classification and a quadratic-weighted kappa of 0.84 for mRS prediction. The findings indicate that the combination of varied learning paradigms and data types in one model improves predictive power and interpretability and opens the door to more patient-centered stroke treatment.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-004

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
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