EANNMHO – A Novel Ensemble Based Technique for Liver Cirrhosis Detection

M.R, Vinutha; Chandrika, J.

doi:10.3991/ijoe.v17i04.21027

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This study investigates whether the use of Harris hawks to calibrate the weights and biases of ANNs -and by so doing to facilitate in the diagnosis of liver diseases -can make a contribution to medical epidemiology within this specific domain. [23,24].…”

Section: Related Workmentioning

confidence: 99%

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Al-safi,

Alsalihi,

Munilla

2024

icisna

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This research paper presents a new method for diagnosing liver disease using the Harris Hawks Optimization (HHO) algorithm in combination with an Artificial Neural Network (ANN). The HHO algorithm enhances the ANN's performance in liver disease classification by refining its parameters. Clinical, laboratory, and demographic data are collected from hepatitis patients and individuals without hepatic illness. The dataset is processed to handle missing values, outliers, and normalization. The HHO algorithm optimizes the weights and biases of the ANN, facilitating the identification of relevant features for accurate diagnosis. The trained ANN model is evaluated using various performance metrics, demonstrating its effectiveness in diagnosing liver disease. The HHO algorithm efficiently searches the entire search space, enhancing the ANN's ability to learn complex patterns and make accurate predictions. Evaluation metrics indicate that the optimized ANN model outperforms traditional machine learning methods, showcasing its potential as a reliable diagnostic tool. Interpretability metrics, such as feature importance and saliency maps, provide insights into the key elements of diagnosis. The proposed approach shows high diagnostic accuracy and interpretability, implying the potential for a stable decision-aid system in clinical practice. Early detection and timely intervention enabled by this method can lead to increased patient safety rates and optimized resource allocation.

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Section: Related Workmentioning

confidence: 99%

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Al-safi,

Alsalihi,

Munilla

2024

icisna

View full text Add to dashboard Cite

show abstract

“…The study's findings show that when a Support Vector Machine with Radial Basis Function (SVM-RBF) is coupled with Principal Component Analysis (PCA) at a threshold of 0.96 PCA, the best degree of accuracy (88.7%) and Area Under the Curve (AUC) (0.91) are attained. Also, research on disease detection using the ensemble method of machine learning achieved a high-performance accuracy rate [12], [13], [14].…”

Section: Literature Reviewmentioning

confidence: 99%

High Performance for Predicting Diabetic Nephropathy Using Stacking Regression of Ensemble Learning Method

Muflikhah,

G. Nurfansepta,

A. Bachtiar

et al. 2024

Int. J. Onl. Eng.

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Diabetes may lead to several problems, one of the most prevalent and deadly of which is diabetic nephropathy. Therefore, the condition represents a significant threat to one’s health since it has the potential to cause irreversible harm to the kidneys’ ability to operate. A significant portion of the research that is being conducted now is focused on determining how accurately diabetic people may be predicted to develop kidney illness. Considering this, the research suggests a regression stacking approach for predicting albumin levels. These albumin values will serve as a reference for the incidence of diabetic nephropathy disease. They will be derived from the medical records of patients. The utilization of stacking regression from three different ensemble approaches, using Random Forest and CatBoost regressors, while the Huber algorithm is used as a meta-learner. The accuracy with which the combination of parameters that are employed is determined is a significant factor. It contributes to the high degree of performance that the ensemble approach achieves. Therefore, in this investigation, a grid search was carried out to tune the hyperparameters of both regressor models. We evaluated the performance of the proposed model using accuracy, MAPE, RMSE, and MSE values. The experimental findings demonstrate great performance. Three selected variables including quantitative UACR, semi-quantitative UACR, and urinary creatinine, achieved high performance. Overall, the performance obtained an accuracy rate of more than 98% with an error rate (MAPE, RMSE, and MSE values) of less than 1%. In conclusion, the stack regressor model can be implemented to predict diabetic nephropathy using clinical datasets.

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EPCA—Enhanced Principal Component Analysis for Medical Data Dimensionality Reduction

Vinutha¹,

Chandrika²,

Balachandran

et al. 2023

SN COMPUT. SCI.

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EANNMHO – A Novel Ensemble Based Technique for Liver Cirrhosis Detection

Cited by 3 publications

References 7 publications

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

High Performance for Predicting Diabetic Nephropathy Using Stacking Regression of Ensemble Learning Method

EPCA—Enhanced Principal Component Analysis for Medical Data Dimensionality Reduction

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