2018· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
A detailed discussion on the AI-based personalized healthcare recommendation systems, their designs, procedures, and uses before 2018 will look at how machine learning algorithms have been used to process patient data in the form of electronic health records (EHRs), wearable sensor data, and genomic data.
Abstract
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.
Future research focuses on integrating Internet of Medical Things (IoMT) devices, real-time monitoring, and federated learning to enable privacy-preserving collaboration across healthcare institutions.
R. T· International Journal of App...· 0 citations
Smart healthcare systems can be viewed as a paradigm shift in the contemporary medical practice, since modern placement of sensing technologies, electronic health records (EHRs), and predictive machine learning (ML) technologies have become a common practice that facilitates proactive, personal, and efficient healthcare delivery. Such trends as the growing rate of chronic illnesses, the aging trend, and escalating healthcare costs have led to a desperate demand of smart systems able to diagnose and predict risks early, as well as make the optimal clinical decision. Predictive machine learning predictive models are models based on historical and real-time healthcare data that reveal the latent patterns, predict the disease progress, and assist clinicians to make evidence-based decisions. In this paper, predictive machine-learning has been adopted to analyze in detail the concept of smart healthcare systems, which involve system architecture, data acquisition, feature engineering, model development, and performance evaluation. The suggested framework combines the wearable sensor data, clinical records, and demographic information into a single analytics pipeline. Some of the machine learning algorithms under analysis and supervised or unsupervised, like logistic regression, support vector machines, random forests, gradient boosting, and deep neural networks, are discussed in the context of healthcare prediction tasks, such as disease risk prediction and hospital readmission prediction and patient outcome prediction. Moreover, the paper also touches on the issues associated with data quality, privacy, security, model interpretability and ethical issues. Recent experimental findings indicate that predictive ml-based healthcare systems are much better than conventional rule-based healthcare systems in terms of prediction accuracy and decision support. The results also show the promise of predictive machine learning to turn healthcare into either a reactive treatment or a preventive, personalized healthcare, as part of the vision of smart and sustainable healthcare ecosystems.
Chinedu Okafor· International Journal of Art...· 0 citations
The digitalization of healthcare is driving a shift from generalized dietary models to personalized nutrition. Traditional approaches fail to account for individual metabolic variations, which reduces their effectiveness in chronic disease prevention. The objective of this paper is to analyze artificial intelligence systems in generating personalized dietary recommendations, evaluate their clinical efficacy, address data standardization challenges, and explore ethical implementation aspects. The study employs a systematic review of scientific sources, a comparative analysis of clinical trials—specifically randomized controlled trials—and conceptual modeling of the operational data flow. It was established that dietary personalization operates as a dynamic 5-step loop: multimodal data collection, AI-driven analysis, prediction, recommendation generation, and real-time feedback. Certain artificial intelligence models (XGBoost, Stacking) demonstrated a metabolic risk prediction accuracy of up to 96.07%. Dietary assessment utilizing computer vision and deep learning improves food recognition accuracy by 30% when accounting for national context and geolocation.Clinical data confirm that dietary recommendations developed using artificial intelligence models achieve a 39% reduction in irritable bowel syndrome symptoms and a 72.7% diabetes type 2 remission rate. Thus, the efficacy of incorporating artificial intelligence technologies into personalized recommendations for health improvement and symptom reduction in chronic diseases has been proven, with results demonstrating significant enhancements in patients' metabolic parameters and life quality.The necessity of data standardization and regulatory framework development for the successful implementation of personalized nutrition into clinical practice is also emphasized. Ethical issues regarding privacy and accountability in AI applications are highlighted, alongside the importance of explainable artificial intelligence in fostering patient trust in digital health solutions. Ultimately, the prospects for personalized nutrition as a new paradigm in dietary science with the potential to substantially improve public health and well-being approaches are outlined.Received 12.07.2026, accepted 21.08.2026, published 22.09.2026
Z. Burova, Maksym Hudzenko, Y. Kryzhova et al.· Human and nation s health· 0 citations
Artificial Intelligence (AI) is fundamentally reshaping healthcare delivery by enabling systems that are predictive, preventive, and personalized. This paper synthesizes recent advances in machine learning, deep learning, natural language processing, and generative AI, and examines how these technologies are applied across disease forecasting, clinical decision support, medical imaging, remote patient monitoring, and precision medicine. Drawing on published literature and industry evidence, the paper presents a conceptual framework linking multi-modal health data, AI analytics, and three interdependent pillars of next-generation care, culminating in improved patient outcomes and sustainable health-system performance. The discussion further evaluates real-world deployments, summarizes representative performance findings reported in the literature, and critically examines persistent barriers including algorithmic bias, data privacy, explainability, and regulatory compliance. The paper concludes that responsible, human-centered AI adoption—anchored in governance, transparency, and equitable design—is essential to realizing intelligent, patient-centered, and sustainable healthcare systems for the future.
Aditya Swaprakash Gadepalli Sri Pratyak· Natural Resources for Human...· 0 citations
: Currently, one of the most significant domestic issues affecting our country is health care. A healthy start in life depends on access to healthcare. Seeing a doctor, however, can be difficult if you have any health issues. Smart Health Forecasting based on the indicators that users or patients provide to the system, a machine learning and predictive modeling system forecasts their illnesses. The healthcare sector and other industries generate a significant amount of data. Humans cannot handle such vast amounts of data quickly enough to diagnose illnesses and recommend remedies. To reduce this human work and generate dependable results, we have investigated machine learning algorithms and data management strategies. Additionally, it improves a number of features of healthcare applications by providing a thorough explanation of medical data. The latest, most powerful technologies will reduce the physical work that experts have to do. Collecting patient, diagnostic, and other data is its primary objective. Entering, storing, accessing, and changing patient and physician data as needed is the system's primary duty. Information about the patient, including their diagnosis, is input into the system and shown on the screen. The only way to access the health prediction system is with a username and password. It can be accessed by a receptionist or administrator. Processing is fairly fast, and the data is safely stored for personal use.
V. Sawant, Swati Nadkarni· Proceedings of the 1st Inter...· 0 citations
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
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