An Expert Health Care System Providing Recommendation

Abstract

Data mining is the method for finding unknown values from enormous amount of data. As the patient’s population increases the medical databases also increasing every day. The transactions and investigation of these medical data is difficult without the computer-based analysis system. The computer-based analysis system indicates the mechanized medical diagnosis system. This mechanized diagnosis system supports the medical practitioner to make good decision in treatment and disease. Data mining is the huge platform for the doctors to handle the huge amount of patient’s datasets in many ways such as make sense of complex diagnostic tests, interpreting previous results, and combining the dissimilar data together. In today\'s computerized world considering automatic and dynamic requirements healthcare system should be more efficient by predicting the disease and providing appropriate medications through user friendly mobile applications. This study aims mainly for the health concerns and the ones who want to be their own Doctor. It is an interactive service for users who wants to know about what health issues they are going through as per the symptoms. It is easy to access and use for searching medicines for the diseases predicted.

Introduction

Healthcare organizations generate vast amounts of data daily, and analyzing this data can help detect diseases early and improve patient care. The proposed system aims to save patients’ time by predicting diseases based on symptoms entered by the user. Using data mining and predictive algorithms, the system analyzes symptoms and identifies possible diseases such as polio, dengue, lung disease, and blood cancer. It also recommends suitable doctors, making it user-friendly and practical for real-time healthcare support.

The system relies on data mining—part of the Knowledge Discovery in Databases (KDD) process—which includes clustering to group similar symptoms and prediction algorithms to determine diseases. As healthcare data is large and complex, AI and Machine Learning (ML) techniques are used to improve diagnosis, reduce human error, and enable personalized care. Expert systems and clinical decision-support systems help doctors by analyzing medical history, symptoms, and test results, although traditional systems face issues such as data heterogeneity and lack of explainability.

Research in this field has evolved from rule-based expert systems to modern AI-driven models. Existing techniques include rule-based reasoning, case-based reasoning, machine learning models (SVM, neural networks, decision trees), and hybrid systems combining multiple approaches. While many studies demonstrate high predictive accuracy, they often focus on specific diseases, require large datasets, or lack real-time usability.

The literature review shows that existing systems often suffer from limitations such as insufficient disease coverage, lack of actionable recommendations, absence of doctor or medicine suggestions, limited scalability, and privacy concerns. The proposed hybrid system addresses these gaps by integrating symptom-based disease prediction with a recommendation engine capable of suggesting medicines and nearby doctors.

Comparative analysis highlights that while past systems excel in prediction, many fail to integrate user-friendly features such as real-time access, personalized suggestions, or secure online deployment. The proposed model combines the strengths of data mining, ML, and recommendation algorithms to create a comprehensive online health support system.

Current challenges include data privacy and security risks, lack of explainable AI, limited real-time monitoring, high computational requirements, and insufficient clinical validation. Research gaps exist in integrating patient history, genetics, lifestyle factors, multilingual interfaces, and large-scale deployment.

Future directions emphasize explainable AI models, integrating IoT and wearable devices, improved security frameworks, personalized healthcare, telemedicine integration, and extensive clinical validation. Addressing these areas will enable advanced, accurate, and secure healthcare recommendation systems for real-world use.

Conclusion

The proposed Expert Health Care Recommendation System effectively integrates data mining and intelligent decision-making techniques to provide accurate disease predictions and medical recommendations. The system enables patients to input their symptoms online and receive real-time analysis, suggested medicines, and nearby doctor recommendations, improving healthcare accessibility—especially in situations where immediate consultation with a doctor is not possible. By implementing a hybrid data mining model, the system enhances the accuracy and reliability of disease classification and supports decision-making in clinical environments. The incorporation of modules for patient login, symptom analysis, and automated recommendation makes the system user-friendly and efficient. Although privacy, security, and authentication remain key challenges, the proposed framework provides a foundation for developing secure and intelligent health prediction systems in the future. Further improvements can be achieved by integrating advanced machine learning algorithms, real-time patient monitoring, and stronger data encryption mechanisms. Overall, the system contributes to the development of intelligent, accessible, and secure healthcare solutions, bridging the gap between patients and healthcare providers through technology-driven innovation.

References

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Copyright

Copyright © 2025 Dr. P. R. Deshmukh, Ms. M. P. Khyade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.