EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis
Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction abilit...
Main Authors: | Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Akash Kumar Bhoi, Paolo Barsocchi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-07-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/14/4036 |
Similar Items
-
Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier
by: Sushruta Mishra, et al.
Published: (2020-11-01) -
A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches
by: Sunil Kumar Mohapatra, et al.
Published: (2021-06-01) -
Wetland Classification, Attribute Accuracy, and Scale
by: Kate Carlson, et al.
Published: (2024-03-01) -
Analysis of Accuracy Metric of Machine Learning Algorithms in Predicting Heart Disease
by: Sajad Yousefi, et al.
Published: (2023-04-01) -
Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau
by: Jessica da Silva Costa, et al.
Published: (2021-01-01)