Machine Learning–based Multi-Disease Prediction

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.

Diabetes Prediction System Using Machine Learning

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.

Clinical Biomarker-Based Prediction of Chronic Kidney Disease Using Explainable Machine Learning

Chronic kidney disease (CKD) is a progressive disease that needs to be diagnosed properly to slow the progression of the disease and its complications. The authors of this study suggest a clinical biomarker-based prediction framework that can be improved with explainable machine learning to enhance the accuracy and interpretability of the classification of CKD. Prior to the development of the models, the publicly available CKD dataset consisting of 400 patient records and 25 clinical attributes was preprocessed by imputing missing values, encoding categorical features, and normalizing the data. The performance of a range of supervised machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Extreme Gradient Boosting (XGBoost) was assessed using the standard performance measures. The best predictive model of the models evaluated was the Random Forest classifier. Explainable Artificial Intelligence (XAI) was used to incorporate the contribution of each biomarker and hemoglobin, serum creatinine, packed cell volume, specific gravity, and albumin were found to be the most significant biomarkers. The results show how a combination of explainable ML and accessible clinical biomarkers can offer a precise, transparent, and clinically interpretable framework for early CKD diagnosis, risk stratification, and informed clinical decision making.

A Multi-Model Machine Learning Approach for Predictive Analytics and Intelligent Decision Making

Disease risk can be predicted and clinical decisions can be made early based on wearable sensor data of physiological and behavioral parameters. However, patterns in wearable data of disease development are mostly non-linear, time-dependent, and influenced by individual health variations. Therefore, it is challenging to get reliable results with a single machine learning model for disease risk prediction. This study introduces a multi-model framework using wearable sensor data to improve early disease risk prediction and support timely clinical decisions. First, the data is processed using a data cleaning, normalization, missing value handling, feature extraction and class balancing. Then, a variety of predictors such as Support Vector Machine, Random Forest, Extreme Gradient Boosting and Long Short-Term Memory networks are trained and tested. The prediction probabilities of the single models are integrated in a stacking-approach. An intelligent decision making component classifies users into different disease risk categories (low, moderate, high) based on predicted probabilities and on clinical decision making thresholds. The framework is complemented by Explainable AI to identify the most influential physiological parameters for individual user risk scores. Performance is measured by accuracy, precision, recall, F1-score, area under ROC curve, sensitivity, specificity and by calibration. In comparison to individual machine learning models, the proposed multi-model framework enables more robust and interpretable disease risk predictions and supports health monitoring, early clinical interventions as well as evidence-based clinical and healthcare decisions.

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

A Multi-Class Heart Disease and Stroke Risk Classification Framework for Combined Assessment: A Machine Learning Approach

This study introduces a combined multi-class machine learning system in predicting both heart disease and stroke risks. This was achieved by creating a harmonized dataset by merging two independent cardiovascular cohorts via conditionbased feature alignment and bias-controlled balancing to be able to make consistent risk modeling across diseases. The suggested framework, in contrast to the previous single-disease methodology, creates a single, explainable prediction pipeline that uses routinely available clinical biomarkers such as cholesterol, fasting glucose, and blood pressure to create detailed risk profiles of the individual patient. Several algorithms were compared and LightGBM demonstrated better performance with stroke and heart disease recall rate being 0.91 and 0.90 respectively with a total of 89% accuracy and an AUC of approximately 0.99. In addition to the predictive performance, interpretability studies based on SHAP values demonstrated similar clinical relevance of the major predictors and also showed cross-disease feature interactions. These results provide an indication that an integrated, interpretable, and data-efficient learning system has the potential to support screening and risk of cardiovascular disease in the early stages of care and inform preventive care in a real healthcare environment.