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

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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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.

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.

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Heart Attack Risk Prediction Using Machine Learning

"Heart Attack Prediction System using Machine Learning that analyzes medical attributes such as age, cholesterol, blood pressure, and other health indicators to predict heart attack risk."

The Heart Attack Prediction System is a web app that assesses the risk of heart attacks using health data provided by users. It employs machine learning models to analyze different factors and deliver risk predictions.

A machine learning-based heart attack prediction system using XGBoost, Flask, and a user-friendly web interface to assess cardiovascular risk

Heart Attack Risk Tracker A machine learning–powered application that predicts the likelihood of a heart attack using Support Vector Machine (SVM) classification. This project leverages health-related datasets and applies preprocessing and model training to deliver accurate predictions.

Heart-Attack-Risk-Prediction-Using-ML is a machine learning-based project designed to predict the risk of a heart attack in a patient over the next 10 years. By analyzing key health indicators such as age, BMI, blood pressure, heart rate, and blood glucose levels, the model provides a percentage risk score.

We built a heart attack risk prediction project using patient data and machine learning. With diagnostic, predictive, and prescriptive analytics, we identified key risk factors and provided preventive insights. Results were visualized in an interactive Power BI dashboard.

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... can be considered. The objective of this study is to optimize the prediction of heart disease using Machine Learning Models.

Explore heart attack patterns, risk factors, and predictive modeling using Python and PySpark. Cleanse and analyze large datasets with efficiency, conduct exploratory data analysis, and deploy machine learning for predictions. Ideal for personal exploration and customization.

The heart is vital for human survival, yet heart disease is the leading global cause of death. With rising cases, addressing this issue is crucial. This application uses inputs like age, BMI, blood pressure, heart rate, and glucose levels to predict a patient’s 10-year heart attack risk as a percentage.

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A machine learning project that predicts the risk of a heart attack (High / Low) from patient health parameters. The pipeline includes data preprocessing (imputation and scaling), class imbalance handling with SMOTE, model training using Random Forest and XGBoost, evaluation metrics, and a simple real-time prediction interface for new patient input

"A machine learning-based Heart Attack Risk Prediction system using Random Forest and a multi-layer Neural Network. It analyzes medical features like age, cholesterol, blood pressure, BMI and lifestyle factors, achieving 88% accuracy. Includes data preprocessing, model training, and Flask web deployment."

this project is web based and predict heart attack risk.

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In this project, I conducted EDA and used Decision Trees, Random Forest, SVM, and Neural Networks to predict heart attack risks, achieving notable accuracy. This showcases my skills in EDA, algorithm implementation, model comparison, and evaluation, highlighting data science's potential in healthcare for early risk assessment.

Heart Attack Risk Prediction Using Machine Learning

This is a simple and interactive web application that predicts the risk level of a heart attack based on user health inputs like age, heart rate, and blood sugar levels. The app is built using Streamlit and a Logistic Regression machine learning model trained directly inside the app.

Analyzed a heart attack risk dataset using statistical analysis and machine learning. Built and evaluated models including logistic regression, LDA, QDA, Naive Bayes, and KNN to identify key risk factors such as age, cholesterol, BMI, diet, blood pressure, and family history.

Heart Attack Risk Prediction Using Machine Learning