Omnicare: A Comprehensive ML/DL-Based Prediction System for Healthcare and Agriculture

Authors

  • Mallikarjuna Nandi Assistant Professor, Department of CSE, RGUKT, Ongole, Andhra Pradesh, India. Author
  • M. Vinitha Assistant Professor, Department of CSE, RGUKT, Ongole, Andhra Pradesh, India. Author
  • Dr. B. NagarajaNaik Assistant Professor, Department of CSE, RGUKT, Ongole, Andhra Pradesh, India. Author
  • B.Yedukondalu Naik Student, Department of CSE, RGUKT, Ongole, Andhra Pradesh, India. Author
  • G.Dhramateja Student, Department of CSE, RGUKT, Ongole, Andhra Pradesh, India. Author

DOI:

https://doi.org/10.47392/IRJASH.2024.041

Keywords:

Full stack, Crop Recommendation, Plant disease identification, Heart Risk Assessment, Disease prediction, Diabetes Prediction, Brain Tumor Detection, Agricultural Prediction, Healthcare Prediction, Deep Learning, Machine Learning

Abstract

Effective decision-making in healthcare and agriculture can be challenging due to the complexity and volume of data involved. Traditional methods often require extensive manual analysis and domain-specific expertise, which can be time-consuming and prone to error. The challenge is compounded by the need for real-time insights and accurate predictions to address critical issues such as disease diagnosis and crop management. Omnicare tackles these challenges by using advanced machine learning and deep learning technologies to facilitate predictions. With pretrained models, it delivers precise insights for medical and agricultural needs, eliminating the need for user-provided training data. This streamlined, user-friendly approach enhances decision-making and represents a significant advancement in applying AI to real-world problems

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Published

2024-10-15