Findings indicate that the proposed architecture is capable of generating reliable and consistent educational recommendations while reducing redundant AI requests through recommendation caching, and contributes to the development of intelligent health information systems.
Abstract
The increasing prevalence of non-communicable diseases, such as diabetes, hypertension, and metabolic disorders, highlights the need for accessible health screening services. However, existing health screening systems generally provide examination results without offering automated educational recommendations to support preventive healthcare. This study proposes an Artificial Intelligence (AI)-based health screening information system that automatically generates fitness and nutrition recommendations based on categorized health screening results. The research employed a Research and Development (R&D) approach using the Extreme Programming (XP) software development methodology. The proposed system was developed using the Laravel framework and the Filament administration panel and integrates a Large Language Model (LLM) through the OpenAI API. The proposed architecture combines structured prompt engineering, predefined AI guardrails, SHA-256 prompt hashing, and recommendation caching to improve recommendation consistency and computational efficiency. The system was evaluated using User Acceptance Testing (UAT) and an AI Recommendation Consistency Evaluation. The UAT results showed that all functional requirements were successfully fulfilled. The consistency evaluation demonstrated that repeated processing of identical health screening data produced stable recommendations, achieving an average qualitative consistency score of 90.4% and an average TF–IDF cosine similarity of 0.784. These findings indicate that the proposed architecture is capable of generating reliable and consistent educational recommendations while reducing redundant AI requests through recommendation caching. This study contributes to the development of intelligent health information systems by introducing an efficient AI recommendation architecture that supports digital health screening and preventive healthcare services.
An AI-based system for predicting diseases along with recommending remedies is presented by employing Random Forest algorithm and considering the patients’ symptoms in addition to age, gender, blood pressure, blood sugar, cholesterol, BMI, heart rate, hemoglobin, stress, and sleep duration.
Kavya B G and Dr. Kruthi R· International Journal of Adv...· 0 citations
The ClinicalML uses advanced machine learning algorithms to analyze patient data and identify health conditions accurately using important parameters like age, BMI, blood pressure, and glucose levels to assist in early disease prediction and personalized treatment support.
G. Vamsi, K. Devendra· International Scientific Jou...· 0 citations
The recent fast progress of artificial intelligence (AI) has changed the general situation in the sphere of healthcare dramatically as the creation of smart systems that can provide individual medical advice is possible. The conventional health care models are largely based on standardized treatment regimens and hence they seldom take into account individual differences that could be in genetic, physiological and behavioral aspects. This drawback has resulted in the development of AI-based personalized healthcare recommendation systems that are intended to give specified interventions, foretelling revelations, and adaptive treatment plans to individual patients. The current paper is a detailed discussion on the AI-based personalized healthcare recommendation systems, their designs, procedures, and uses before 2018. The paper will look at how machine learning algorithms like supervised learning, unsupervised learning and hybrid models have been used to process patient data in the form of electronic health records (EHRs), wearable sensor data, and genomic data. These systems have also been improved in terms of scale and efficiency with the integration of big data analytics and cloud computing. Other critical topics that are being discussed in the paper include data heterogeneity, privacy issues, model interpretability and clinical validation. It particularly focuses on such methods of recommendation as collaborative, content-based, and customized approaches to recommendations. Mathematical expression of prediction model, and measure of similarity are discussed to give a theoretical basis of system design. In addition, the paper measures the performance of the system through measures like accuracy, precision, recall and patient satisfaction indices. A comparative study helps to point out how well AI-based systems can be effective in terms of bettering health results, decreasing readmission rates, and increasing the effectiveness of the decisions made by clinicians. According to the results, AI-powered personalized healthcare can transform the field of patient care and make it proactive, preventive, and precision medicine. Nonetheless, challenges of ethics, regulations and technical issues must be overcome to achieve success in implementation. The conclusion of this paper presents the future directions of research to enhance the robustness, ease-of-interoperability, and clinical adoption of systems.
Tendai Chikore· International Journal of Mod...· 0 citations
Diabetes is a major global health concern due to its rising prevalence and the significant number of undiagnosed cases. Early detection using Electronic Health Records (EHRs) has become a key research focus, supported by advances in machine learning and artificial intelligence. This study presents a systematic review of hybrid intelligent models for early diabetes detection using EHR data from 2015 to 2026. The review analyzes various hybrid approaches, including ensemble learning, deep learning, Fuzzy logic-based, feature selection techniques, and optimization-based models. It also examines commonly used datasets, feature extraction methods, and evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Findings indicate that hybrid intelligent models generally outperform traditional machine learning methods by improving predictive accuracy and capturing complex nonlinear relationships in clinical data. However, issues such as missing data, lack of standardization, interpretability, and limited external validation remain present. The study highlights the need for explainable AI, federated learning, and multimodal data integration to improve clinical applicability. Overall, this review provides insights into current methodologies and identifies future directions for developing more robust, scalable, and clinically applicable diabetes prediction systems.
Umoh Augustine Uduak· Journal of Artificial Intell...· 0 citations
This first scoping review of AI applications in hypertension health education identified a mismatch between rapid advances in generative AI and the limited availability of rigorous clinical evidence.
Haoran Chen, Shenglan Xiao, Tong Wan et al.· Journal of Medical Internet...· 0 citations
This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information and demonstrates the ability to generate safe and personalized diet recommendations.
Usha Kamale, M. Pujashree, T.Siri Chandana et al.· International Journal of Com...· 0 citations
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