Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques

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Year : 2026 | Volume : 15 | 01 | Page :
    By

    Altaf Osman Mulani,

  • Vaibhav Godase,

  • Swapnil Takale,

  • Assistant Professor,

  1. Professor, Department of Electronics & Telecommunication Engineering, Maharashtra, India
  2. Assistant Professor, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering, Maharashtra, India
  3. Assistant Professor, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering, Maharashtra, India
  4. Assistant Professor, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering,, Maharashtra, India

Abstract

The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and advanced imputation strategies. Multimodal features—including clinical, demographic, and behavioral data—were selected using a hybrid feature selection algorithm combining Recursive Feature Elimination (RFE) and Mutual Information Gain. The prediction models included voting ensembles, weighted bagging with Extra Trees, XGBoost, and a neural network meta-learner within a stacked architecture. Hyperparameter optimization and model selection were automated using the Tree-based Pipeline Optimization Tool (TPOT). The performance was evaluated through 10-fold stratified cross-validation using metrics such as accuracy, ROC-AUC, precision, recall, and F1-score. The hybrid ensemble with AutoML optimization achieved the highest performance with 92.3% accuracy and 0.94 ROC-AUC on the kidney disease dataset. The results highlight the potential of combining classical ML ensembles with AutoML to enhance predictive healthcare solutions. Real-world deployment issues such as latency, model interpretability, and patient privacy were also addressed. The study demonstrates that this approach not only improves classification metrics but also offers a more adaptable and scalable solution for intelligent disease diagnosis.

Keywords: Hybrid Ensemble, AutoML, Disease Prediction, Feature Selection, Medical Data Classification

How to cite this article:
Altaf Osman Mulani, Vaibhav Godase, Swapnil Takale, Assistant Professor. Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques. Research and Reviews : Journal of Computational Biology. 2025; 15(01):-.
How to cite this URL:
Altaf Osman Mulani, Vaibhav Godase, Swapnil Takale, Assistant Professor. Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques. Research and Reviews : Journal of Computational Biology. 2025; 15(01):-. Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=234676


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79. Saurabh Singh, Karm Veer Arya, Ciro Rodriguez Rodriguez, and Altaf Osman Mulani, Emerging Trends in Artificial Intelligence, Data Science and Signal Processing, Communications in Computer and Information Science (CCIS), volume 2440.
80. Saurabh Singh, Karm Veer Arya, Ciro Rodriguez Rodriguez, and Altaf Osman Mulani, Emerging Trends in Artificial Intelligence, Data Science and Signal Processing, Communications in Computer and Information Science (CCIS), volume 2439.

Ahead of Print Subscription Review Article
Volume 15
01
Received 19/07/2025
Accepted 26/11/2025
Published 23/12/2025
Publication Time 157 Days
137

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