Identification and Prediction of Chronic Diseases Using Machine Learning Approach

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.