An Enhanced Symptom Clustering with Profile Based Prescription Suggestion in Biomedical application

Area of Science:

• Biomedical Engineering
• Data Mining
• Health Informatics

Background:

• Increasing data volumes in biomedical databases present challenges for content retrieval.
• Current systems lack personalized prescription recommendations based on patient profiles and disease identification.

Purpose of the Study:

• To develop a prescription recommendation system for the biomedical field.
• To address challenges in retrieving specific information from large biomedical datasets.
• To enable personalized prescription suggestions through profile-based analysis.

Main Methods:

• Data preprocessing and cleansing techniques for handling noise and missing data.
• A hybrid feature selection algorithm for efficient data retrieval.
• Profile-based analysis combining user and professional suggestions for disease identification.

Main Results:

• Improved retrieval of specific content from large biomedical databases.
• Enhanced accuracy in generating prescription recommendations tailored to individual patient profiles.
• Demonstrated superiority over existing approaches in data retrieval and recommendation quality.

Conclusions:

• The proposed system offers a significant advancement in personalized healthcare by enabling accurate prescription recommendations.
• Effective data preprocessing and hybrid feature selection are crucial for managing large biomedical datasets.
• The system provides a robust solution for profile-based suggestions, improving patient care.