Skip to content

An intelligent disease prediction framework integrating machine learning, optimization, and LLM with RAG

Jul 2026 · Network Modeling Analysis in Health Informatics and Bioinformatics · Vol 15 · 0 citations · 43 references

TL;DR

A framework that integrates multiple machine learning models and optimization algorithms to enable the early prediction of cardiovascular disease, Parkinson’s disease (PD), and nonalcoholic fatty liver disease (NAFLD) and has high potential for adoption in health-care applications driven by artificial intelligence.

View source

Similar papers

Open access Jul 2026

Multiple Disease Prediction System using Machine Learning Algorithm

This study presents a Multiple Disease Prediction System (MDPS) that predicts the likelihood of various diseases using patient health information and medical datasets and achieves satisfactory predictive performance across multiple disease categories.

Prachi Kumari · 0 citations
Open access Aug 2026

An Advanced Ensemble Framework for Robust Heart Disease Detection and Classification

A robust Ensemble Learning (EL) framework for the prediction and classification of CVD by integrating multiple ML algorithms with a DL component using an Artificial Neural Network employed as a feature extraction layer prior to ensemble aggregation is presented.

El Haddad Khadija, A. Bekkari, W. Bouarifi et al. · 0 citations
Review Open access Aug 2026

Comprehensive machine learning approaches for disease prediction current applications recent advances and future prospects

The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available.

Tehreem Khan, Tayyaba Usman, Ifrah Khalid et al. · 0 citations
Conference Jul 2026

Machine Learning–based Multi-Disease Prediction

This paper proposes a machine learning–based multi-disease prediction system that integrates disease-specific classifiers within a unified, real-time clinical decision-support platform. The framework employs Support Vector Machine (RBF) for diabetes prediction, Support Vector Machine (linear) for heart disease, Decisio...

Chandrasekar.M, Y. S, Adithiyaa K.B · 0 citations
Open access Aug 2026

Maximum Value Attribute-Based Extra Trees, XGBoost, and LightGBM for Early Disease Prediction

Experimental results show that the hybrid MVA based models outperforms the traditional classifiers in terms of prediction accuracy, computational efficiency and feature optimization.

Basharat Ahmad Hassan, Babar Saeed, Khurram Gulzar et al. · 0 citations
Review Open access Aug 2026

Artificial Intelligence for Early Heart Disease Prediction: A Review of Machine Learning Techniques

There is an urgent need for explainable, clinically validated and standardised ML frameworks to translate predictive models into routine healthcare practice and improve early detection of cardiovascular disease.

Hanna Rasheed, Arya.K.R Arya.K.R, Ashida.K.A Ashida.K.A · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.