An ensemble learning-based model for effective chronic kidney disease prediction

Kumari, Sweety; Singh, Sunil K.

doi:10.1109/icccis56430.2022.10037698

Cited by 4 publications

(1 citation statement)

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“…Additionally, we utilize stacking and voting ensembles of SVM, RF, Adaboost, LDA, and MLP models to achieve precise and efficient CKD and non-CKD event predictions. Performance metrics such as accuracy, precision, recall, F1 score, and ROC were used to assess the effectiveness of the models [14].…”

Section: Related Workmentioning

confidence: 99%

An efficient IoT-Artificial Intelligence-based Chronic Kidney Diseases Prediction using Temporal Convolutional Network (TCN) Deep Learning Method in Healthcare System

Rahul Sanmugam Gopi,

Tavanam Venkata Rao,

Uma Maheswari Sethuramasamy

et al. 2023

ARASET

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IOT (Internet of Things) can control the remote based patient’s health care monitoring system and here monitoring for the chronic kindney diseases predication levels. When an IoT device collects data from patients, it sends the data to a software application that can be viewed by healthcare professionals and/or patients. Ongoing Chronic Kidney Diseases (CKD) is one of the main supporters of the bleakness and mortality of non-transferable illnesses, influencing 15-20% of the total populace. Early and exact finding of CKD stages is accepted to be fundamental to decrease the effect of unexpected problems on patients, for example, hypertension, sickliness (low blood count), bone mineral issues, poor nourishing wellbeing, and irregularities. Since our requirement is better disease classification for Temporal Convolutional Network (TCN) DL (Deep Learning) methods, the problem of machine learning method is that the existing system cannot detect the level of disease. Our get the Chronic Kidney Disease dataset get from kaggle minimum 1000 and 10000 datas has been used the preprocessing of input datasets. The feature extraction using Latent Dirichlet Allocation (LDA) and this algorithm has been process for specially used for text data or information extraction. This method can be used for perfect extract text data form CKD disease dataset. After extract the data and then approach the classification method of TCN algorithm. In this algorithm to classify the CKD disease level accuracy find out here. Our classification algorithm for TCN examine the extreme performance of pointers being an accuracy.

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Section: Related Workmentioning

confidence: 99%