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Diagnosing heart attack risk with logistic regressiion and decision tree algorithms

Aslı Göde & Adnan Kalkan

International Journal of Healthcare Technology and Management2024https://doi.org/10.1504/ijhtm.2024.10068279article
AJG 1
Weight
0.30

What the paper says

The number of people who lose their lives due to heart attacks around the world and in our country is increasing day by day. Treatment and early intervention are important for people who have a heart attack and have a chance of survival. When immediate medical attention is provided and appropriate treatment is administered, the survival rate increases. For this reason, this study aimed to diagnose the risk of heart attack early by using machine learning methods. The dataset used includes 303 patient data and 14 features. The data was trained using logistic regression and decision tree algorithms. As a result of the training, a success rate of 83.8% and 77% was achieved, respectively. The logistic regression model gave the better success result.

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https://doi.org/https://doi.org/10.1504/ijhtm.2024.10068279

Or copy a formatted citation

@article{aslı2024,
  title        = {{Diagnosing heart attack risk with logistic regressiion and decision tree algorithms}},
  author       = {Aslı Göde & Adnan Kalkan},
  journal      = {International Journal of Healthcare Technology and Management},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1504/ijhtm.2024.10068279},
}

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Diagnosing heart attack risk with logistic regressiion and decision tree algorithms

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.