The study evaluates several machine‑learning classifiers for predicting heart disease using two public datasets, each with 14 health‑related attributes. Performance metrics such as MAE, RAE, accuracy, precision, recall, and F‑measure were used, revealing that SVM performed best on the UCI data while Simple Cart excelled on the Kaggle data. The authors conclude that well‑tuned ML models can aid early heart‑disease diagnosis and suggest future work on hybrid methods and newer datasets.
arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.
The paper presents an intelligent system that predicts stroke risk using eleven clinical features and evaluates seven supervised machine learning algorithms. Ensemble methods—Random Forest, Stacking Classifier, and Bagging Classifier—achieved the highest accuracies, reaching 99.52%, while other models such as KNN, TabNet, and a custom feedforward network also performed well. The study demonstrates that ensemble approaches are particularly effective for stroke classification tasks.
Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early diagnosis and preventive measures can greatly reduce...
arXiv:2607. 02142v1 Announce Type: cross Abstract: Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities.
arXiv:2310. 00505v3 Announce Type: replace-cross Abstract: Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems.
arXiv:2407. 20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments.
arXiv:2606. 26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver.
arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.
arXiv:2601. 00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using routinely collected electronic health record (EHR) data and benchmark their performance against the FIB-4 and APRI clinical scores.
arXiv:2607. 09948v1 Announce Type: cross Abstract: Cardiac amyloidosis (CA) is increasingly recognized but remains substantially underdiagnosed, because its clinical and imaging phenotype overlaps with more common cardiomyopathies.
arXiv:2606. 30665v1 Announce Type: cross Abstract: Stage B heart failure is characterized by asymptomatic structural or functional cardiac abnormalities.
arXiv:2509. 11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.