Jul 2026· International Scientific Journal of Engineering and Management· 0 citations
TL;DR
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.
Abstract
The ClinicalML is an artificial intelligence-based healthcare system designed to assist in early disease prediction and personalized treatment support. It uses advanced machine learning algorithms to analyze patient data and identify health conditions accurately. The system focuses on common diseases such as diabetes, hypertension, and cardiovascular disorders using important parameters like age, BMI, blood pressure, and glucose levels. ClinicalML follows a two-stage approach in which Random Forest, Gradient Boosting, and Multi-Layer Perceptron (MLP) models are combined using a soft voting ensemble method for accurate disease classification. Based on the predicted disease, the system provides personalized diet plans and medication schedules. The complete system runs within a web browser without requiring backend servers or cloud infrastructure, ensuring faster processing, better privacy, and easy accessibility. ClinicalML also includes charts and graphs to help users and doctors understand the results and make informed healthcare decisions quickly.
Keywords: AI in Healthcare, Disease Prediction, Diet & Drug Recommendation, Random Forest, Gradient Boosting, Multi-Layer Perceptron, Soft Voting Ensemble
AI and ML are being utilized for enhanced treatment, diagnosis, and prediction of diseases. However, most of the applications provide generalized treatment based on the symptoms provided with lesser emphasis on patient’s health factors. In this paper, we present an AI-based system for predicting diseases along with recommending remedies. This is achieved 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. Further, the suggested information includes the causes, diagnostic tests, diet chart, exercise, lifestyle, home remedies, prevention measures, and specialist consultation. The proposed application has been implemented as a web application with custom API and stack, user authentication, language translation, electronic health records, admin panel, and PDF report generation. The experimental results show that the Random Forest algorithm is effective in fitting the data with accurate prediction of diseases. The proposed AI-based tool can be considered as an intelligent companion for screening diseases with improved awareness and prediction for taking necessary precautions and seeking medical treatment.
Kavya B G and Dr. Kruthi R· International Journal of Adv...· 0 citations
The rapid growth of medical data and the increasing complexity of clinical diagnostics requires effective computational frameworks to help people in healthcare industry. This paper presents an integrated intelligent healthcare system which is aimed at improving disease prediction and personalized drug recommendations. Based on the recent advancements in Artificial Intelligence, the proposed system uses hybrid deep learning architecture. First, it makes use of Transformer-based Natural Language Processing (NLP) models to analyze clinical symptoms and Electronic Health Records (EHR), which greatly increases diagnostic accuracy compared to traditional decision-tree methods. Second, the system combines the Graph Neural Networks (GNNs) with an ontology driven databases like DrugBank and UMLS to provide safe and effective drug recommendations. This module specifically models drug to drug interactions and checks for safety using OpenFDA label data, which ensures reliable treatment planning. By merging symptom-based disease inference with the reinforcement learning based personalization, this approach fills some of the significant gaps in automated healthcare. It offers scalable solution for precision medicine and better resource allocation.
Gayathri Tippani, Datta Sai, K. Sai et al.· 2026 6th International Confe...· 0 citations
Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.
Sunidhi, Mothe Rahul, M. Kumar et al.· International Journal for Re...· 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
An AI-assisted healthcare kiosk, a web-based, fully offline system that provides automated disease prediction, medication recommendations, BMI assessment, cardiovascular risk evaluation, and doctor referrals without needing a permanent physician or internet connection is discussed.
Thanu Shree M. N, M. Vijayalakshmi· International Research Journ...· 0 citations
The Heart Stroke Prediction System is a machine learning-based application developed to predict the risk of heart
stroke using patient health information. The system helps users identify potential stroke risks at an early stage through
intelligent data analysis. It considers important health parameters such as age, gender, hypertension, heart disease, BMI,
smoking status, glucose level, and work type. The collected data is preprocessed and analyzed to improve prediction accuracy. A
Logistic Regression algorithm is used to classify whether a person is likely to experience a stroke or not. The model is trained
using historical healthcare data and evaluated with testing datasets. The application is developed using Python and Flask to
provide a simple and user-friendly web interface. Users can enter their health details and receive instant prediction results. The
system also includes a BMI calculator and basic health guidance to improve awareness. It supports early diagnosis and
encourages preventive healthcare measures. The proposed system reduces manual assessment time and provides quick decision
support. It offers an efficient, accurate, and cost-effective solution for heart stroke risk prediction. Overall, this project
demonstrates how machine learning can assist healthcare professionals and individuals in improving early stroke detection and
promoting better health management.
G. Kumar, S. Gafur· International Journal for Re...· 0 citations
