Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

--

Cardiovascular diseases,Myocardial infarction,Pulmonary thromboembolism,Aortic stenosis,Stenosis cardiology,Exploratory data analysis,Artificial intelligence,Machine learning,Data mining,Prediction

• Clinical Medicine - Medical and Health Sciences
• Health Sciences - Medical and Health Sciences
• Other Medical Sciences - Medical and Health Sciences

This publication is an output of the following project(s):

• AI-based mobile applications for public health response