Hybrid diabetes disease prediction framework based on data imputation and outlier detection techniques-Reference-Cited by-同舟云学术

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Hybrid diabetes disease prediction framework based on data imputation and outlier detection techniques

Published:2021-08-19 Issue: Volume: Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Srivastava Anand Kumar¹²,Kumar Yugal²^ORCID,Singh Pradeep Kumar³^ORCID

Affiliation:

1. Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

2. Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat India

3. Department of Computer Science KIET Group of Institutions, Delhi‐NCR Ghaziabad India

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12785

Reference85 articles.

1. A new machine learning technique for an accurate diagnosis of coronary artery disease

2. A Novel Intelligent Medical Decision Support Model Based on Soft Computing and IoT

3. A segment-based framework for modeling and mining data streams

4. A Multi-Patient Data-Driven Approach to Blood Glucose Prediction

5. A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines

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1. Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration;Applied Sciences;2024-08-23

2. Variable Neighborhood Search Based Human Learning Optimization Algorithm for Secure Data Analysis and Computing;SN Computer Science;2024-05-17

3. Intelligent Decision-Making in Diabetes Diagnosis: An Exploration of Multi-Granularity Three-Way Decisions;2023 International Conference on Cyber-Physical Social Intelligence (ICCSI);2023-10-20

4. Supervised Learning Approaches on the Prediction of Diabetic Disease in Healthcare;Intelligent Systems;2023-10-06

5. Revolutionizing Diabetes Disease Prediction Through Novel Machine Learning Techniques;Nano;2023-07-28

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