Forecasting Health Vulnerabilities through Machine Learning
DOI:
https://doi.org/10.54741/ASEJAR/4.6.2025.179Keywords:
deep learning, data collection, analysis, machine learning, NLP, CNN, ANNAbstract
Machine Learning Model to Forecast Patient Disease Vulnerability using Data Cleanroom Methodology enable to join Patient data anonymously coming from Patient diagnosed with different disease. Cross Organization data analytics helped to identify pattern in multiple dieses in Patients to train the model. So model can forecast if Patient has one dieses what are chances of having other dieses in future and take preventive actions Based on this project the aim is to investigate the effectiveness of deep learning techniques in the early identification of disease. Two things must be done to accomplish this goal: first, it must be made clear how important it is to promptly identify disease outbreaks in the current global health setting, and second, deep learning techniques must be subjected to a thorough evaluation of how well they perform in this critical job. These goals are part of the study's overall effort to get a thorough understanding of the range of deep-learning approaches that can assist and improve disease outbreak investigation processes. The cutting-edge technology that has supported early intervention and lessened the effects of disease epidemics in the global community is improved by this research.
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