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This study introduces a machine learning approach for early chronic disease prediction using patient symptoms and lifestyle data. The system utilizes convolutional neural networks (CNN) and K-nearest neighbors (KNN) for accurate disease identification and prognosis.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Modern lifestyles and environmental factors contribute to a rise in chronic diseases.
  • Early disease identification is crucial for effective management and prevention of severe health outcomes.
  • Manual disease diagnosis by clinicians can be challenging and prone to inaccuracies.

Purpose of the Study:

  • To develop and evaluate a machine learning system for the accurate identification and prediction of common chronic illnesses.
  • To leverage advanced computational techniques for reliable patient categorization regarding chronic disease risk.
  • To enhance the process of disease prognosis through automated analysis of patient data.

Main Methods:

  • Implementation of a machine learning model integrating Convolutional Neural Network (CNN) for automated feature extraction and disease prediction.
  • Utilization of K-nearest Neighbors (KNN) algorithm for precise distance calculations and data matching.
  • Dataset preparation included patient symptoms, lifestyle habits, and doctor consultation details.

Main Results:

  • The proposed system demonstrates effective disease prognosis based on symptom analysis.
  • The combined CNN and KNN approach achieved reliable identification of chronic diseases.
  • Comparative analysis showed the system's performance against traditional algorithms like Naïve Bayes, decision tree, and logistic regression.

Conclusions:

  • Machine learning, particularly CNN and KNN, offers a powerful tool for early and accurate chronic disease prediction.
  • Integrating diverse patient data, including symptoms and lifestyle, improves predictive accuracy.
  • The developed system provides a valuable approach to support clinical decision-making in managing chronic illnesses.