GitHub

Heart Disease Prediction using Logistic Regression

heart-disease-logistic-regression/ ├── data/ # dataset (heart_disease_uci.csv) ├── src/ # source code │ ├── heart_disease_logistic_regression.py ...
Beebom

AI vs Machine Learning: What is the Difference?

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
Scientific Research Publishing

Analysis of Decision Support Systems (DSS) Integration with AI Using Mobile Health (m-Health) and Wearables ()

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PC Gamer

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A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
IEEE

IoT-Enabled Heart Disease Risk Prediction Using Machine Learning Algorithms: A Comparative Analysis of ML Models

Abstract: This Study will explore how the IoT and machine learning predict heart disease risks through real-time wearable device and sensor data. The Cleveland and Hungarian datasets have relevant ...
Frontiers

Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors

Introduction: Cardiovascular health is increasingly at risk due to modern lifestyle factors such as obesity, smoking, stress, hypertension, and sedentary behavior. Post-pandemic health practices and ...
BMJ

Predicting cardiovascular events from routine mammograms using machine learning

10 The George Institute for Global Health, School of Public Health, Imperial College London, London, UK Background Cardiovascular risk is underassessed in women. Many women undergo screening ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Introduction: Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as ...
News Medical

AI and routine lab tests offer a more accurate prediction of genetic disease risk

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai ...