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Diabetes Diagnosis using Electronic Health Records through Machine Learning

  • Unique Paper ID: 172712
  • Volume: 11
  • Issue: 9
  • PageNo: 679-686
  • Abstract:
  • Electronic Health Records (EHRs) have become a critical part of modern healthcare systems. They offer numerous benefits such as improved patient care, better clinical decision- making, and increased efficiency. However, the vast amounts of data generated by EHRs can be overwhelming for healthcare professionals, making it difficult to extract meaningful insights. This is where machine learning (ML) techniques can be applied to help make sense of the data. The applications of these EHRs could easily be interpreted/found in numerous cases and could also be seen as a life-changing tool while trying to diagnose the specifics of a disease. Through the course of this project, EHRs of patients and their vitals are taken as the factors/bases for diagnosing Diabetics that affect the way your body regulates blood sugar or glucose. Metabolic disorder that occurs in diabetic patients is considered as an indirect cause of many diseases may it be Cardiovascular diseases, Kidney diseases, Eye problems, Neuropathy, Infections or Alzheimer’s. This project aims to develop an ML-based system for analysing EHR data to improve patient outcomes. The system will be trained using supervised and deep learning techniques using ML algorithms like KNN, SVM and RF to perform tasks such as predicting patient outcomes, identifying potential health risks, and thus finding out the important insights from the data. The system will be developed using Python programming language and various ML libraries such as Pandas, NumPy and Scikit- learn. The project will also involve data pre- processing, data cleaning, and data normalization to ensure the data is suitable for ML algorithms. The system will be evaluated using real-world EHR data and compared with existing methods used by healthcare professionals. The project's success will be measured based on the system's accuracy, efficiency, and ability to improve patient outcomes. Overall, this project aims to demonstrate the potential of ML in healthcare and how it can be used to improve patient care and outcomes. The results of this project could have significant implications for healthcare providers and patients.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 9
  • PageNo: 679-686

Diabetes Diagnosis using Electronic Health Records through Machine Learning

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