The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset efficiently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that influence the possibility of heart diseases. This project emphasizes to evaluate different algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set

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

Heart attack prediction using Machine Learning.

Heart Attack Prediction Using Machine Learning Algorithm

We have made a tool for the prediction of Heart Attack using Machine Learning Model. We have also made a website for the same. The website will take input paramaters related to their health like blood pressure, occurrence of chest pain etc. from the user, and based on it, will provide the output (chances of getting heart attack) from our model on the website.

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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we predict that the patient is suffering with heart attack or not

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.

Heart Attack Prediction Using Machine Learning

Heart attack prediction system using machine learning

Heart Attack Prediction using Machine Learning Models

Heart Attack Analysis & Prediction using Machine Learning

Heart Attack Analysis & Prediction Dataset by using Machine Learning

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.

Developed a prediction system to predict heart attack based on data of 300 patients and 13 major characteristics obtained from UCI’s machine learning repository and used some of the popular Machine Learning algorithms like Decision Trees, Random Forest, Support Vector Machines, Neural Networks and K Nearest Neighbors for prediction and provided detailed comparative analysis.

It helps the doctor to monitor the patient outside of clinical settings. Monitors vitals such as heart rate, spo2, body temperature, electric activity of heart and use a machine learning model made using sklearn python module to make a prediction about heart attack and alert the doctor. Implements live location tracking to send help in emergencies.

Heart Attack Predictor

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This project helps to predict a person will have a heart attack or not by processing his symptoms with stored dataset. The main process is to load libraries and cleveland dataset, dataset cleaning, train and test model, logistic regression algorithm used, confusion matrix to check the correctness and efficiency of model.

This project focuses on predicting heart attack risks using two machine learning models: Decision Tree and Multilayer Perceptron (MLP). By combining four public datasets and optimizing hyperparameters, we built models capable of predicting heart attack risks with high accuracy. The project is designed to improve early detection and prevention of he

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Heart Attack Risk Prediction Using Machine Learning

Heart Attack Prediction using Machine Learning algorithms

Heart Attack Prediction using machine learning algorithm

Heart Attack prediction using machine learning and dashboarding

Heart attack prediction using various Machine Learning Algorithm

Heart_Attack_Prediction_System using Machine Learning with python

Heart Attack Prediction analysis and also different type of machine learning algarithms using