Hybrid Method for Evaluating Feature Importance for Predicting Chronic Heart Diseases
Authors:
- 28 September 2022
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
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.Keywords
This publication has 14 references indexed in Scilit:
- Hybrid Metric K-Nearest Neighbor Algorithm and ApplicationsMathematical Problems in Engineering, 2022
- Comparative Analysis of the Results of EMG Signal Classification Based on Machine Learning Algorithms2021
- Comparison of feature importance measures as explanations for classification modelsSN Applied Sciences, 2021
- A New Approach to Classifying Myocardial Infarction and Cardiomyopathy Using Deep Learning2020
- Evolutionary Feature Scaling in K-Nearest Neighbors Based on Label Dispersion Minimization2020
- Algorithm of Automatic Differentiation of Myocardial Infarction from Cardiomyopathy based on Electrocardiogram2020
- Feature selection and classification systems for chronic disease prediction: A reviewEgyptian Informatics Journal, 2018
- An Improved Hybrid RFVIM-KNN Method for High Dimensional Data2016
- A Feature-Scaling-Based $k$ -Nearest Neighbor Algorithm for Indoor Positioning SystemsIEEE Internet of Things Journal, 2015
- Dynamic K-Nearest-Neighbor with Distance and attribute weighted for classification2010