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

Website with django to predict using ML model if the person at low or high risk for Heart Attack

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Heart attack predictor / classifier with ML models

ML project used to predict heart attack with the help of heart condition

ML model to predict heart attack risk with 91.8% accuracy using health metrics.

Python EDA looking at the physiological factors contributing to heart attacks for over 300 patients. After looking at multiple ML predictive models, able to predict the likelihood of a heart attack with up to 92%

Heart Attack Prediction System 🏥 is a production-ready ML platform for predicting heart attack risk with real-time APIs, multiple model support, and full MLOps integration. It features automated training, monitoring, data drift detection, and scalable deployment with Kubernetes and Docker.

Dyslipidemia, a condition with abnormal lipid levels in the blood, significantly increases the risk of cardiovascular diseases like heart attacks and strokes. This project aims to build accurate models for predicting dyslipidemia using both machine learning (ML) and deep learning (DL) techniques. The primary focus is on maximizing recall to minimiz

This is a web application which uses multiple ML models, Python(Flask and NLTK) in the backend for predicting heart attack percentage of a person, Front-End is designed through HTML/CSS & JS, also integrated a chatbot for seamless user experience which replies with common diseases treatment recommendations.

This is a web application which uses multiple ML models, Python(Flask and NLTK) in the backend for predicting heart attack percentage of a person, Front-End is designed through HTML/CSS & JS, also integrated a chatbot for seamless user experience which replies with common diseases treatment recommendations.

Heart_Attack_Risk Predictor with Eval ML

ML model predicting heart attack risk using SVM, KNN and Logistic Regression with full EDA pipeline

Heart Attack Prediction Model: Random Forest Classifier predicts heart attack likelihood with 90.2% accuracy. Built with feature engineering and hyperparameter tuning, showcasing proficiency in ML techniques.

ML classification pipeline predicting heart attack risk using patient health and lifestyle indicators. Built on Azure ML Studio with EDA, feature selection, and hyperparameter tuning.

Heart disease is a leading cause of mortality worldwide, and early detection is crucial in preventing fatal heart attacks. With advancements in machine learning (ML), predictive models can help assess the risk of heart attacks based on various health parameters. This blog explores a Heart Attack Prediction Systems.

Machine learning model for heart attack risk prediction using patient health records. This project applies data preprocessing, feature engineering, and various ML models to predict heart attack risks with high accuracy.

Predict heart attack risk using ML. Explore various algorithms, then streamline with EvalML AutoML for efficient model selection and hyperparameter tuning. Enhance early intervention and prevention.

Heart attack rate prediction using Machine Learning involves analyzing patient health data such as age, blood pressure, cholesterol, heart rate, and lifestyle factors. ML models learn patterns from historical medical records to predict the likelihood or risk level of a heart attack, helping doctors with early diagnosis and preventive care.

Statistical analysis with advanced visualization and ML implemented by Random Forest and SVM algorithms which predict the chance to heart attack according to sex, smoking/non-smoking, blood pressure, and more other measures.

On Device ML Model inference using Tensorflow Interpreter, Heart Attack Risk Predictor[HARP] is designed to work with and without internet, when there is no internet, the model inference is achieveing by invoking Tensorflow interpreter

This is a web application which uses multiple ML models, Python(Flask and NLTK) in the backend for predicting heart attack percentage of a person, Front-End is designed through HTML/CSS & JS, also integrated a chatbot for seamless user experience which replies with common diseases treatment recommendations.

An AI-powered system that predicts the risk of heart attack using health data such as age, cholesterol, and blood pressure. Built with Python, ML algorithms, and Flask, it provides early warnings for preventive healthcare. Designed as a decision-support tool for patients and medical professionals.

An end-to-end machine learning project that predicts the risk of heart attack using clinical patient data. This project focuses on building a reliable, interpretable, and healthcare-aware ML pipeline, with special emphasis on minimizing false negatives. This repository contains my individual contribution (KNN implementation) for the group project.

The incidence of Heart Disease has been steadily increasing over the years. Medical practitioners are able to use medical technology such as cardiac catheterization and electrocardiograms for the diagnosis of heart conditions. However; the ever expanding amount of data collected by medical and healthcare practitioners has provided opportunities to improve the outcomes of patients diagnosed with or at risk for heart disease. This study uses machine learning methods to predict coronary heart disease/heart attack and identify the relationship between coronary heart/heart attack and health indicators. The source of the data for the study was the Heart Disease Data Set from the UCI Machine Learning(ML) Repository. The dataset consists of 303 instances, a subset of 14 out of the 75 attributes were examined. The attributes consist of categorical, binary and numeric values. Subjects in the study experienced a vascular event (i.e., myocardial infarction (MI) or syncopal event). Exploratory analysis was performed on the data which showed that there is a relationship between gender and diabetes, gender and diabetes and 'chol'(Cholesterol) and 'trtbps'(Resting BP). Several data-mining algorithms (such as Logistic Regression, k-Nearest Neighbors, Random Forest, Gradient Boosting, Naive Bayes and Support Vector Machine) were used to develop classifiers and determine their accuracy. The study showed the Naive Bayes model performed the best in predicting MI with an overall accuracy of 0.85, a sensitivity of 0.84 and a specificity of 0.87.

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/