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The app created with Python to predict person's heart health condition based on well-trained machine learning model (logistic regression).

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Use Cases Limitations Evidence Owner's Insight

In this project, you can use logistic regression to predict person's heart health condition expressed as a dichotomous variable (heart disease: yes/no).

See owner's GitHub repository for more information: https://github.com/kamilpytlak/heart-condition-checker/blob/main/LICENSE

The model was trained on approximately 70,000 data from an annual telephone survey of the health of U.S. residents from the year 2020. The dataset is publicly available at the following link: https://www.cdc.gov/brfss/annual_data/annual_2020.html. The data is originally stored in SAS format. The original dataset contains approx. 400,000 rows and over 200 variables. The data conversion and cleaning process is described in another repository: https://github.com/kamilpytlak/data-analyses/tree/main/heart-disease-prediction.

This application was not uploaded by the author, but through their publicly available Github Repository, https://github.com/kamilpytlak/heart-condition-checker/blob/main/LICENSE.

MIT License

Copyright (c) 2022 Kamil Pytlak

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


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  • Clinical Informatics

Owner

K Kamil Pytlak

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