heart-attack-prediction

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heart-attack-prediction

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A Heart Attack Risk Prediction Project

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<div align="center"><h1>Heart Attack Risk Prediction</h1></div>

# About

This project aims to predict the likelihood of a heart attack using a data set that includes various health and lifestyle factors. The prediction is based on the identification of key features that contribute significantly to the assessment of heart attack risk.

## Features

The dataset includes the following features:

- **Patient ID:** Unique identifier for each patient
- **Age:** Age of the patient
- **Sex:** Gender of the patient (Male/Female)
- **Cholesterol:** Cholesterol levels of the patient
- **Blood Pressure:** Blood pressure of the patient (systolic/diastolic)
- **Heart Rate:** Heart rate of the patient
- **Diabetes:** Whether the patient has diabetes (Yes/No)
- **Family History:** Family history of heart-related problems (1: Yes, 0: No)
- **Smoking:** Smoking status of the patient (1: Smoker, 0: Non-smoker)
- **Obesity:** Obesity status of the patient (1: Obese, 0: Not obese)
- **Alcohol Consumption:** Level of alcohol consumption by the patient (None/Light/Moderate/Heavy)
- **Exercise Hours Per Week:** Number of exercise hours per week
- **Diet:** Dietary habits of the patient (Healthy/Average/Unhealthy)
- **Previous Heart Problems:** Previous heart problems of the patient (1: Yes, 0: No)
- **Medication Use:** Medication usage by the patient (1: Yes, 0: No)
- **Stress Level:** Stress level reported by the patient (1-10)
- **Sedentary Hours Per Day:** Hours of sedentary activity per day
- **Income:** Income level of the patient
- **BMI:** Body Mass Index (BMI) of the patient
- **Triglycerides:** Triglyceride levels of the patient
- **Physical Activity Days Per Week:** Days of physical activity per week
- **Sleep Hours Per Day:** Hours of sleep per day
- **Country:** Country of the patient
- **Continent:** Continent where the patient resides
- **Hemisphere:** Hemisphere where the patient resides
- **Heart Attack Risk (Outcome):** Presence of heart attack risk (1: Yes, 0: No)

## Methodology

The predictive model will focus on selecting the most informative features from the dataset to improve the accuracy of heart attack risk predictions. By analyzing and prioritizing key factors, the model aims to provide valuable information to identify individuals at higher risk of having a heart attack.

## Contact

Bayram Alper KILIÇ - [@alperrkilic](https://www.linkedin.com/in/bayram-alper-kilic/) - alperkilicbusiness@gmail.com  
[![LinkedIn][linkedin-shield]][linkedin-url]
[![Github][github-shield]][github1-url]
[![Gmail][gmail-shield]][gmail1-url]

Pelin KOZ - [@pelinkoz](https://github.com/pelinkoz) - pel0652@gmail.com

[![LinkedIn][linkedin-shield]][linkedin2-url]
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[contributors-shield]: https://img.shields.io/github/contributors/alperrkilic/heart-attack-prediction.svg?style=for-the-badge
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[contributors-url]: https://github.com/alperrkilic/heart-attack-prediction/graphs/contributors
[forks-url]: https://github.com/alperrkilic/heart-attack-prediction/network/members
[stars-url]: https://github.com/alperrkilic/heart-attack-prediction/stargazers
[issues-url]: https://github.com/alperrkilic/heart-attack-prediction/issues
[license-url]: https://github.com/alperrkilic/heart-attack-prediction/blob/master/LICENSE.txt

[linkedin-url]: https://www.linkedin.com/in/bayram-alper-kilic/
[linkedin2-url]: https://www.linkedin.com/in/pelin-koz-5a2874222/

[github1-url]:https://github.com/alperrkilic
[github2-url]: https://github.com/pelinkoz

[gmail1-url]: mailto:alperkilicbusiness@gmail.com
[gmail2-url]: mailto:pel0652@gmail.com

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