Disease Prediction Model Using Multi-Modal Data Fusion
Authors:-Shruti Deokule, Dipti Kause, Suhani Korde, Suhani Korde
Abstract- With recent developments in machine learning and healthcare informatics Strong disease prediction models have been made possible . In order to improve the accuracy and dependability of early disease diagnosis, we present a multi-modal data fusion system in this paper. Advanced fusion techniques that can lessen the drawbacks of single-modality models are used to integrate heterogeneous data sources, such as wearable sensor readings, genomic data, medical images, and electronic health records (EHR). Our method integrates crucial information from multiple datasets by combining feature selection, preprocessing, and ensemble learning. In comparison to the traditional models, we find that the experimental results produce 15% higher prediction accuracy and lower error rates—down to 2.3% for cases of chronic disease.
href=”https://doi.org/10.61137/ijsret.vol.11.issue2.351″>10.61137/ijsret.vol.11.issue2.351
