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

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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-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.

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.

Heart attack risk prediction using traditional ML models and H2O AutoML on clinical data.

Machine learning models to predict heart attack risk using health and lifestyle data. Predictive analytics project to classify individuals at risk of heart attack. Healthcare ML project: Heart attack risk prediction using demographic, lifestyle, and medical data.

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.

Heart attack risk prediction using retinal images with ML/DL models and a Flask-based web interface for real-time analysis.

A Python-based Heart Attack Prediction System using Machine Learning. It takes medical inputs like age, cholesterol, blood pressure, ECG values, etc., and predicts heart attack risk using a trained ML model. Includes a clean Tkinter GUI, dataset handling, preprocessing, model training, accuracy evaluation, and real-time prediction.

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.

Heart Attack Risk Prediction using SVM: This project applies a Support Vector Machine model to predict heart attack risk based on medical features like age, chest pain, cholesterol, and more. It includes data analysis, SVM implementation, and performance evaluation to demonstrate the role of ML in healthcare.

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.

"Heart Attack Prediction project using healthcare data with features like age, BMI, lifestyle, and medical history. Performed EDA to uncover key patterns and trained ML models achieving 100% accuracy. Includes data preprocessing, visualization, and model evaluation for early risk detection."

A machine learning model that predicts heart attack risk based on patient health data. The project compares multiple ML models (Logistic Regression, Random Forest, XGBoost) and uses SMOTE for dataset balancing and PCA for feature selection to improve predictions.

In This projest I've used the following models for the predictions : Logistic Regression Decision Tree Random Forest K Nearest Neighbour SVM In order to predict the result and see which ML model suits best in predicting the risk of having a heart attack.