Predicting Kidney Failure using an Ensemble Machine Learning Model: A Comparative Study

Haque, Md Sabbirul; Amin, Md. Shahedul; Ahmad, Sabbir; Sayed, Md Abu; Raihan, Aqib; Hossain, Mohammad Anwar

doi:10.1109/eecsi59885.2023.10295641

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2024

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Cited by 3 publications

References 24 publications

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A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

Naseer,

Amjad,

Raza

et al. 2024

IEEE Access

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A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

Naseer,

Amjad,

Raza

et al. 2024

IEEE Access

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Machine Learning Approaches in Optical Sensing for Precision Kidney Function Analysis

Divya,

Vasuki

2024

2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)

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A Novel Ensembled Deep Machine Learning Framework for the Prediction of Chronic Disease

Hegde,

Mundada

2024

Artificial Intelligence Transformations for Healthcare Applications

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The state of the environment and human behaviour today contribute to a wide range of diseases that affect people. To avoid such diseases reaching their worst, it is crucial to recognise and anticipate them in their early stages. Medical professionals sometimes find it challenging to appropriately detect disorders by manual procedures. The main intension of the proposed work is to detect and forecast patients having chronic diseases, such as chronic kidney disease, at their early stage. In this chapter, a novel ensemble model has been implemented by combining machine learning model with deep learning model. The combination of the ensembled machine learning and deep learning model is termed as ensembled deep machine learning framework (EDPMLF). The performance of the implemented EDPMLF have been validated using various parameters such as confusion matrix, Precision, AUC score, recall, and accuracy. The experimental investigation reveals that the proposed framework attained higher accuracy compared to the traditional approaches.

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Predicting Kidney Failure using an Ensemble Machine Learning Model: A Comparative Study

Cited by 3 publications

References 24 publications

A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages

Machine Learning Approaches in Optical Sensing for Precision Kidney Function Analysis

A Novel Ensembled Deep Machine Learning Framework for the Prediction of Chronic Disease

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