"Heart Attack Analysis" - A data science project for predicting heart attacks using machine learning on health-related data.

This repository contains a machine learning model for predicting the risk of heart attacks based on basic health parameters. The model is trained on a dataset of over 10,000 patients sourced from Kaggle and utilizes the XGBoost algorithm. Additionally, a Flask web application is provided for interactive use, allowing users to input their health.

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.

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Predicting heart attacks using predictive analysis models of machine Learning like decision tree,logistic regression, Random Forest, KNN.

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.

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Compared ML Models Performance Predicting Heart Attacks

SVM applied to Healthcare Industry for early preventive action

Сreating a model for predicting heart attacks

Model for predicting whether people are prone to heart attacks based on their natural and biological characteristics

This project aims to explore the application of data analysis and machine learning techniques in predicting heart attacks.The primary goal is to build a robust predictive model to help in early diagnosis and intervention, improving patient outcomes

Abstract —This paper applies explanatory artificial intelligence (XAI) techniques to interpret a logistic regression model predicting heart attacks. I use Shapley Additive Explanations (SHAP) and Local Interpretable Model-Independent Explanations (LIME) to improve the transparency of model predictions.

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

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Predicting survival of Patients with Heart Failure.

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An exploratory predictive model for heart attacks based on health biometrics and using real world data.

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Using a dataset of patients, I trained a simple machine learning algorithm to predict which of them were at high risk for a heart attack in the near future.

Predicting heart attacks with R

ITP 449 Final project

This repository contains a machine learning model for predicting heart attack risk based on clinical features. The dataset includes 1,319 samples with eight input features and one output label (positive or negative for heart attack risk).

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In this course-end project, the objective was to develop a system to predict heart attacks effectively by analyzing various factors related to cardiovascular health. The dataset provided contained information about these factors, such as age, sex, chest pain type, blood pressure, cholesterol levels, and more.

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Predicting Heart attacks possibilities before they happen