MULTIMODAL MACHINE LEARNING FOR PREDICTING DISEASE PROGRESSION USING CLINICAL AND MEDICAL IMAGING DATA

Abstract

 In precision medicine, accurate prediction of disease progression has become a significant challenge as traditional clinical evaluations are unable to account for complex interactions between variables among the patient's heterogeneous population. The combination of clinical records and medical imaging data using multimodal machine learning offers potential for enhancing prognosis accuracy and personalized treatment planning. We present a practical and compliant predictive framework based on multimodal machine learning combining the structured clinical variables and the medical imaging features for predicting disease progression. The proposed framework relies on attributes of the EHR, laboratory parameters, demographic and radiological imaging parameters derived from MRI and CT scans. Convolutional Neural Networks are used for image feature extraction and Gradient Boosting and Random Forest algorithms are used to process the clinical information. Feature fusion techniques are used to fuse both modalities and create predictive models. A set of patient records, 2500 records, was taken from the past for experimentation. The accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve were used to evaluate four machine learning models: Logistic Regression, Random Forest, XGBoost, and Multimodal Deep Learning. These results show that multimodal learning is effective in boosting the prediction accuracy over unimodal learning. The proposed multimodal framework obtained an accuracy of 92.4%, a precision of 91.6%, a recall of 90.9%, an F1-score of 91.2% and an AUC value of 0.95. Integrated models were shown to be superior in identifying progression patterns and high-risk patients using statistical analysis. The study highlights the applicability of multimodal machine learning in disease prognosis and personalized healthcare systems. The developed framework is a scalable method for clinical decision support and future intelligent healthcare applications.