Use of the Method of Setting the Interval Target in the Problem of Synthesis of a Neural Network Classifier for Diagnosing Chronic Kidney Disease in Patients

Mirkin, Eugene L.; Savchenko, Elena

doi:10.1007/978-3-030-81119-8_6

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, none of the researchers implemented a feature selection framework to dynamically select the features for the model; rather, they selected the features based on suggestions from different scholars in the literature. ML has also been applied in different medical contexts to improve the prediction of other diseases and has recorded a huge increase in accurate results [ 28 , 29 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

Menshawi

Hassan

Allheeib

et al. 2023

Sensors

View full text Add to dashboard Cite

The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%