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A Heart Attack Risk Prediction Project

This project, ‘Heart Stroke Prediction’ is a machine learning based software project to predict whether the person is at risk of getting a heart stroke or not. Heart diseases have become a major concern to deal with as studies show that the number of deaths due to heart diseases has increased significantly over the past few decades in India. World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. Traditionally, they have relied on standard assessments of cholesterol, blood pressure and health conditions such as diabetes to predict whether a patient is likely to suffer a heart attack.

Introduction In my case studies I keep writing in English because it is used in Kaggle and I also keep them in Portuguese because my native language is Brazilian Portuguese, so we can share more knowledge and experiences in Kaggle with our Brazilian colleagues. We will develop and analyze the algorithms with the best capacity and identify the problems in the heart and at the end we will make a comparison between them. Description Context Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. Attribute Information Age: age of the patient [years] Sex: sex of the patient [M: Male, F: Female] ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes, N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] HeartDisease: output class [1: heart disease, 0: Normal] Source This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are: Cleveland: 303 observations Hungarian: 294 observations Switzerland: 123 observations Long Beach VA: 200 observations Stalog (Heart) Data Set: 270 observations Total: 1190 observations Duplicated: 272 observations Final dataset: 918 observations Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Citation fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. Acknowledgements Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

This project presents a system for predicting the risk of heart attack using retinal eye images, leveraging Machine Learning (ML) and Artificial Intelligence (AI) techniques.

Heart Disease Prediction

A machine learning project that analyzes heart health data to predict the risk of a heart attack. It includes data preprocessing, visualization, and model building using algorithms like Logistic Regression and Random Forest to provide accurate and insightful predictions for early diagnosis.

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Cleaning data and predicting heart attack risk for each patient

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Machine learning-based prediction of heart attack risk using Decision Tree, KNN, Logistic Regression, and SVM

Heart Attack Prediction is a machine learning project that analyzes medical parameters to predict the risk of heart attack. It uses data preprocessing, feature selection, and trained ML models to provide accurate predictions and insights for early diagnosis support.

Data Science and (ML) can be very helpful in the prediction of heart attacks in which different risk factors like high BP, high cholesterol, abnormal pulse rate, diabetes can be considered. The objective of this study is to optimize the prediction of heart disease using ML

Heart Attack Prediction Using Machine Learning: This project uses machine learning models to predict the likelihood of heart attacks based on clinical health data, focusing on features like cholesterol, chest pain type, and age. The model aims to help healthcare providers identify at-risk patients for early intervention.

Data Science and machine learning (ML) can be very helpful in the prediction of heart attacks in which different risk factors like high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc.

Heart Attack Risk Prediction

Project

Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively.

Медицинское исследование и веб-приложение с ML-моделью, реализованное с помощью FastAPI и Docker.

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Predicting heart attack risk using machine learning models with data preprocessing, model evaluation, and interpretability using LIME

Streamlit web app to early predict heart attack

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Machine learning case study on predicting heart attack risk using demographic, lifestyle, and clinical data, with Random Forest achieving the best performance.

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