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

This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.

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

In this project, I have prepared a detailed report on predicting and interpreting heart attack risk using various machine learning techniques and approaches, as well as the relationship between environmental features and variables with heart attack risk.

About This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.

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

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

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

Real-time Panic Attack Detection System using Arduino & Machine Learning A research-focused wearable system that collects multi-sensor physiological data (EDA, heart rate, temperature, respiration, motion) via Arduino and analyzes it with machine learning models to predict panic attacks in under 1 second. Features personalized baseline calibration,

A machine learning project that predicts heart attack risk using Random Forest classification. Features data preprocessing, model training, and dual deployment interfaces with FastAPI backend and Streamlit frontend, all containerized with Docker for seamless deployment.

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

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.

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.

This repository contains a machine learning project that predicts the likelihood of a heart attack based on a dataset of 170,501 rows and 25 features. The current model achieves an accuracy of 75%, with ongoing improvements through feature engineering and scaling.

This repository contains a machine learning project that predicts the likelihood of a heart attack based on a dataset of 170,501 rows and 25 features. The current model achieves an accuracy of 75%, with ongoing improvements through feature engineering and scaling.

This project uses machine learning to predict heart attack risk based on health indicators like age, BMI, and medical history. Using the Kaggle dataset, multiple models were evaluated, with AdaBoost achieving 85% accuracy. The goal is to support early diagnosis and preventive care.

This project utilizes machine learning to predict heart attack risk based on health and lifestyle factors. It involves data preprocessing, exploratory analysis, and model training with algorithms like Logistic Regression and Random Forest, aiming for accurate risk classification using evaluation metrics such as accuracy and ROC-AUC.

This is my machine learning course work. I have collected this dataset from kaggle. There are 303 patient records with 14 features. I applied Exploratory Data Analysis methods and nine different machine learning models to predict the heart attack disease with this accuracy: XGBoost: 95.08% AdaBoost: 93.44% MLPClassifier: 93.44% Random Forest: 91.8% Gradient Boosting: 91.8% Logistic Regression: 90.16% SVM: 90.16% KNN: 88.52% Decision Tree: 81.97%. 

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

About our problem 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.

World Heart Day Scale-up prevention of heart attack and stroke cardiovascular diseases (CVDs) take the lives of 17.9 million people every year, 31% of all global deaths. The number is growing day by day as the population is growing rapidly. This effect the poor and developing country the most. But there is hope because in recent times technology is so advanced and this technology accelerated the public health sector by developing advanced functional biomedical solutions. Which will help to make the right decision within a short time. In this project, we are trying to predict the heart disease of a person based on some medical information. Such as age, sex, chest pain (cp), resting blood pressure (trestbps), serum cholesterol in mg/dl (chol), fasting blood sugar (fbs), etc. With the help of those information's our algorithms will create a prediction result if the person suffers from heart disease or not. Which will help the doctors to take quick decisions whatever needs to be done in time. Those machine learning models takes short time to predict the disease with high accuracy. And this is how the machine learning model plays a big role in the medical science field.

ITP 449 Final project

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The heart attack detection model is a machine learning algorithm that predicts heart attack risk with 91% accuracy. By analyzing patient medical histories, vital signs, and other health indicators, it identifies critical patterns and risk factors.

Machine learning project to predict heart attack risk with Streamlit interface.

machine learning models to predict heart attack with accuracy upto 90%.

Application with Machine learning model which help you to predict Heart attack

Predicting whether or not someone is at risk of having a heart attack with machine learning.

This project's main goal is to determine the best machine learning algorithm for predicting heart attacks with the given data.