This project has analysed the different factors which are responsible for heart attack Risk

This project endeavors to develop an effective model for predicting the risk of heart attacks based on a thorough analysis of relevant factors. The insights gained from this analysis will contribute to a better understanding of cardiovascular health and risk factors.

This project applies Probability and Statistics (PnS) concepts to analyze heart attack risks. It involves data cleaning, hypothesis testing, and model training using a classified dataset. Various statistical tests and visualizations are used to extract insights, enhancing predictive accuracy.

This repository contains a Google Colab notebook and dataset dedicated to heart attack risk analysis. Access the notebook to delve into detailed data exploration, statistical analysis, and visualizations aimed at understanding heart attack risk factors

Heart Attack Risk database analysis in R Studio

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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.

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.

Heart Attack Risk Prediction Dashboard using Tableau

This project focuses on developing a predictive model for heart attack risk using historical health data. By analyzing various factors such as age, cholesterol levels etc, the model aims to identify individuals at high risk for heart attacks. The insights generated can guide preventive measures and improve health outcomes through early intervention

Heart Attack Analysis using Kaggle dataset. Exploring risk factors and building predictive models. #datascience #machinelearning #healthcare

A comprehensive analysis and interactive dashboard for exploring heart attack risk factors, treatment outcomes, and the impact of lifestyle choices.

Data mining and pattern analysis projects focused on identifying relationships and risk indicators in heart attack datasets, emphasizing exploratory analysis, association rules, and feature-driven insights.

A Jupyter Notebook that analyzes heart attack-related data including age, gender, cholesterol levels, blood pressure, and risk insights using Python and data analysis techniques.

Its a kaggle dataset which defines Heart Attack Risk data in CSV formatted. I create a logistic model to analysis the data by training and testing the data.

This project focuses on analyzing patient data to predict the likelihood of a heart attack. Using a combination of exploratory data analysis, outlier detection, and machine learning techniques, this project aims to provide insights into the factors that contribute to heart disease and develop a model to accurately predict heart attack risk.

Group project using R and CDC data to predict heart attacks based on health risk factors. Exploratory data analysis, hypothesis testing, decision trees (random forest and bagged), logistic regression, and model evaluation.

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.

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 project is dedicated to predicting heart attack risks using machine learning. It features an Exploratory Data Analysis (EDA) notebook for uncovering patterns in the data and a Model Training notebook for developing and optimizing predictive models.

This repository contains a machine learning-based analysis and prediction system for assessing the risk of heart attacks. Leveraging data-driven insights, the project aims to provide valuable insights into cardiovascular health, contributing to early detection and prevention strategies.

**HeartGuard** is a deep learning-based real-time ECG analysis and risk dashboard trained on **PTB-XL** and **MIT-BIH** clinical data. Utilizing **1D CNN** and **TFLite** models, it detects heart attack and arrhythmia risks instantaneously. The system visualizes vital signs and reports critical anomaly scores via an interactive interface.

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Heart attack risk analysis using clinical and lab features to identify high-risk patients and guide interventions.

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Heart Attack Risk Analysis

Heart Attack Risk Analysis

Heart Attack Risk Analysis

Heart Attack Risk Analysis