Multimodal Machine Learning for Explainable Disease Risk Prediction and Clinical Decision
- 1DKTE Society's Textile and Engineering Institute
Abstract
Early disease risk prediction plays a significant role in modern healthcare by supporting preventive care, timely diagnosis, and informed clinical decision-making. This paper presents a proposed multimodal machine learning framework for explainable disease risk prediction using clinical, demographic, lifestyle, and healthcare data. The framework incorporates data preprocessing, missing-value handling, feature selection, and classification techniques to identify individuals at elevated risk of developing diseases. Various machine learning and deep learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, XGBoost, and neural networks, are considered for comparative predictive modelling. Explainable Artificial Intelligence techniques such as SHAP and LIME are incorporated to provide interpretable predictions by identifying the contribution of individual risk factors. The proposed architecture integrates data processing, predictive modelling, explainability, and a user-friendly interface to support healthcare professionals in making informed decisions. The study also addresses important challenges, including data quality, class imbalance, overfitting, model generalization, privacy, and clinical validation. Future enhancements include incorporating longitudinal electronic health records, wearable health data, federated learning, and advanced multimodal approaches. The proposed framework aims to improve transparency, reliability, and accessibility in AI-assisted disease risk assessment while supporting, rather than replacing, professional clinical judgment.
Keywords: Multimodal Machine Learning, Disease Risk Prediction, Explainable Artificial Intelligence, SHAP, LIME, Healthcare Analytics, Clinical Decision Support, Predictive Modelling.
Related
- An Explainable Multimodal Deep‐Learning Model Using Histopathological Image and Clinical Information for Prognosis Prediction in Colorectal Cancer: A Multi‐Center Cohort Study
- Explainable multimodal learning analytics for model-based counterfactual decision support using literature-calibrated synthetic data
- Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support
- EXPLAINABLE AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEMS IN PRECISION ONCOLOGY: INTERPRETABLE MODELS FOR MULTIMODAL CANCER CARE
- A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data
BibTeX
@misc{devmore2026multimodal,
title = {{Multimodal Machine Learning for Explainable Disease Risk Prediction and Clinical Decision}},
author = {Priti Devmore Priti Devmore and Karan Subhash Chavan Karan and Anmol Subhash Chougule Anmol and Arya Mukund Bhagwat Arya and Ashwin Ratndip Chougule Ashwin},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23616978},
url = {https://zenodo.org/records/23616978},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multimodal Machine Learning for Explainable Disease Risk Prediction and Clinical Decision
AU - Priti Devmore Priti Devmore
AU - Karan Subhash Chavan Karan
AU - Anmol Subhash Chougule Anmol
AU - Arya Mukund Bhagwat Arya
AU - Ashwin Ratndip Chougule Ashwin
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.5281/zenodo.23616978
UR - https://zenodo.org/records/23616978
AB - Early disease risk prediction plays a significant role in modern healthcare by supporting preventive care, timely diagnosis, and informed clinical decision-making. This paper presents a proposed multimodal machine learning framework for explainable disease risk prediction using clinical, demographic, lifestyle, and healthcare data. The framework incorporates data preprocessing, missing-value handling, feature selection, and classification techniques to identify individuals at elevated risk of developing diseases. Various machine learning and deep learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, XGBoost, and neural networks, are considered for comparative predictive modelling. Explainable Artificial Intelligence techniques such as SHAP and LIME are incorporated to provide interpretable predictions by identifying the contribution of individual risk factors. The proposed architecture integrates data processing, predictive modelling, explainability, and a user-friendly interface to support healthcare professionals in making informed decisions. The study also addresses important challenges, including data quality, class imbalance, overfitting, model generalization, privacy, and clinical validation. Future enhancements include incorporating longitudinal electronic health records, wearable health data, federated learning, and advanced multimodal approaches. The proposed framework aims to improve transparency, reliability, and accessibility in AI-assisted disease risk assessment while supporting, rather than replacing, professional clinical judgment. Keywords: Multimodal Machine Learning, Disease Risk Prediction, Explainable Artificial Intelligence, SHAP, LIME, Healthcare Analytics, Clinical Decision Support, Predictive Modelling.
ER -