Hybrid Combination of Machine Learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System

Ganu, Likha; Arun, Biri

doi:10.1007/978-981-19-0840-8_36

Cited by 3 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This result into approximately 97% accuracy. This result is comparable with similar work in the literature including [20], [22] and [24].…”

Section: Results Of Cross-validationsupporting

confidence: 93%

See 1 more Smart Citation

A Neuro-Fuzzy-based Approach to Detect Liver Diseases

Babatunde,

Balogun

et al. 2024

PakJET

View full text Add to dashboard Cite

The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.

show abstract

“…This result into approximately 97% accuracy. This result is comparable with similar work in the literature including [20], [22] and [24].…”

Section: Results Of Cross-validationsupporting

confidence: 93%

“…The use of deep learning (DL) techniques for the classification of liver illness is the main focus of the study in reference [20]. Their work shows that DL networks can enhance classification accuracy, yet there isn't a lot of information available about the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neuro-Fuzzy-based Approach to Detect Liver Diseases

Babatunde,

Balogun

et al. 2024

PakJET

View full text Add to dashboard Cite

show abstract

“…Therefore, the authors discussed sex disparities that exist in ILPD and concluded that biases should be evaluated in the early stages of machine learning to give a vision of inequalities in existing clinical practice. ILPD has received a great deal of attention as an attempt to achieve the highest accuracy in predicting liver disease [48][49][50].…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Analysis of Machine Learning Algorithms on Medical Datasets for Disease Prediction

Elsayed,

Ismail,

Abdel-Gawad

et al. 2024

Information Sci. Appl.

View full text Add to dashboard Cite

This paper aims to analyze and evaluate the performance of different classification algorithms on medical datasets and gives some tips and guidelines for choosing the right algorithm for the dataset. For this purpose, twelve medical datasets were selected from the UCI repository, including breast cancer data, chronic kidney disease, cryotherapy, hepatitis, immunotherapy, Indian Liver Patient Dataset (ILPD), liver disorders, pima diabetes, risk factors cervical cancer, statlog (heart) dataset, dermatology and lung cancer. The selected classifiers for performance evaluation are Bagging, IBK, J48, JRip, Multilayer Perceptron (MP), Naïve Bayes (NB), Support Vector Machine (SVM), ZeroR and Voting Frequency Intervals (VFI). The algorithms were implemented on datasets using the WEKA tool and 10-fold cross-validation. Experimental results and analysis for all measures were introduced to guideline researchers who seek to achieve and how to balance different measures. Additionally, challenges and future directions were presented at the end of this paper.

show abstract

Review on enhancing clinical decision support system using machine learning

Masood,

Naseem,

Rashid

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Clinical decision‐making is a complex patient‐centred process. For an informed clinical decision, the input data is very thorough ranging from detailed family history, environmental history, social history, health‐risk assessments, and prior relevant medical cases. Identifying the need for structured input data to enable clinical decision‐making and quality reporting, such that it is crucial for the end‐users is still a challenge. The Clinical Decision Support Systems (CDSS) enhanced using Machine Learning (ML) approaches are described. CDSS aids in the detection and classification of various diseases but they cannot fully capture the environmental, clinical, and social constraints that are taken into consideration by the clinician in the diagnosis process. The authors provide an overview of state‐of‐the‐art healthcare CDSS. The authors initially collected 3165 research articles for this review out of which approximately 3148 records were identified from databases while 17 records were from other sources. A total of 1309 unique articles obtained from the searches were included in the study which was further rigorously evaluated for final inclusion. A generic architecture of computer‐based decision support systems using ML is provided. However, the study does not include the comparison of these CDSS in terms of their performance because of heterogeneity in the disease type, modality used for diagnosis, and the ML approach used for detection in CDSS.

show abstract

Hybrid Combination of Machine Learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System

Cited by 3 publications

References 28 publications

A Neuro-Fuzzy-based Approach to Detect Liver Diseases

A Neuro-Fuzzy-based Approach to Detect Liver Diseases

Adaptive Analysis of Machine Learning Algorithms on Medical Datasets for Disease Prediction

Review on enhancing clinical decision support system using machine learning

Contact Info

Product

Resources

About