Healthoassist: Predictive Treatment and Medical Recommendation System

Abstract

This paper presents a systematic review of artificial intelligence (AI)-based systems that transform patient symptoms into diagnostic, treatment, and dietary recommendations. Emphasizing Support Vector Machine (SVM) as a core classification tool and other diverse machine learning models, this review synthesizes literature from 2015–2025 to examine methods, datasets, evaluation practices, and deployment considerations. The paradigm shift from monolithic diagnostic tools to integrated patient-management systems is a key focus. We identify critical gaps in end-to-end integration, personalization, data privacy, and model explainability, particularly in a unified symptomto-lifestyle pipeline. Building on these findings, we propose a modular SVM-based architecture that integrates symptom parsing, disease prediction, medication suggestion, personalized diet planning, and user history. This architecture directly addresses the need for a practical, interpretable, and cohesive solution for remote healthcare management.

Introduction

This review examines the use of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, particularly symptom-driven systems that transform patient-reported symptoms into disease predictions, treatment recommendations, and personalized diet/lifestyle plans. These systems are critical for decentralized, low-resource triage but face challenges due to fragmented pipelines and inconsistent recommendations.

Focus on SVM:
Support Vector Machines (SVMs) are highlighted for symptom-based classification due to their strong generalization on small datasets, interpretability, and regulatory explainability. SVMs are proposed as the core classifier in an integrated symptom-to-treatment/diet pipeline, offering a balance between accuracy and transparency compared to deep learning models.

Key Components of the Pipeline:

1. Symptom Preprocessing & Disease Prediction: NLP (e.g., ClinicalBERT) normalizes free-text and structured inputs; SVM or classical ML models classify likely diseases. Deep learning models provide richer representations but are less interpretable.

2. Treatment Recommendation: Combines knowledge-based, collaborative filtering, and causal inference approaches with safety layers to prevent contraindications and drug interactions.

3. Personalized Diet & Lifestyle: Content-based, collaborative, and optimization approaches are used; integration with diagnosis and treatment is often missing in current systems.

4. Datasets & Evaluation: Uses datasets like MIMIC, UCI, and nutrition databases. Emphasis on explainability, privacy (e.g., federated learning), fairness, and clinical evaluation metrics beyond standard ML accuracy.

Research Gaps Identified:

• Fragmented pipelines with little end-to-end integration

• Limited personalization and longitudinal patient use

• Trade-off between interpretability and predictive performance

• Poor generalizability across institutions and populations

• Immature safety layers for cross-module checks

• Lack of standardized clinical evaluation metrics

Proposed Solution:
A modular, SVM-centered system integrates symptom normalization, feature engineering from longitudinal patient history, disease prediction, treatment recommendation, and diet planning. Linear or RBF SVM kernels ensure interpretability and accurate multi-class classification, with calibration and clinician referral mechanisms for safety.

Conclusion

This systematic review has successfully synthesized the contemporary literature on AI-based symptom-to-treatment and diet recommendation systems, highlighting the competitive performance and inherent interpretability advantage of Support Vector Machine models in diagnostic classification. We identified critical gaps, most notably the fragmentation of the end-to-end pipeline and the lack of history-aware personalization. The proposed modular, SVM-centered architecture, integrated with an NLP front-end, a rule-based safety layer, and a dedicated User History module, offers a practical and safety-conscious blueprint for deployable clinical decision support. Future efforts must focus on validating such integrated systems with diverse, longitudinal datasets under strict privacy and regulatory compliance, ensuring the safe transition of AI from research labs to the patient’s bedside.

References

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Copyright

Copyright © 2025 Dr. Atmeshkumar Patel, Prof. Vijay Rakhade, Latish Gajula, Tejas Patil, Vedant Jadhav, Dipak Shinde. 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.