HYBRID METHOD FOR EVALUATING FEATURE IMPORTANCE FOR PREDICTING CHRONIC HEART

Authors

  • Diseases Rashid Nasimov Artificial Intelligence,Tashkent State University of Economics Tashkent, Uzbekistan

DOI:

https://doi.org/10.17605/OSF.IO/MW3D5

Keywords:

Artificial Intelligence, chronic heart diseases, feature importance, disease prediction, decision tree, KNN.

Abstract

Predicting the impact of different factors on the patient’s health is as important as diagnosing diseases, especially when monitoring patients with chronic diseases. To perform this by Artificial Intelligence (AI) methods, it is recommended to determine the features importance (FI) of data. There are a number of methods to evaluate FI. However, we can see a big variation in their results which is difficult to interpret. To solve this issue, we proposed new method which aim is minimizing the differences. Furthermore, to demonstrate the effectiveness of the proposed method we used the extracted FIs as weights of the weighted KNN and compared performances.

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Published

2023-06-27

How to Cite

Diseases Rashid Nasimov. (2023). HYBRID METHOD FOR EVALUATING FEATURE IMPORTANCE FOR PREDICTING CHRONIC HEART. Web of Scientist: International Scientific Research Journal, 4(6), 598–608. https://doi.org/10.17605/OSF.IO/MW3D5

Issue

Section

Articles