Effective Analysis and Diagnosis of Liver Disorder by Data Mining

Kumar, Sanjay; Katyal, Sarthak

doi:10.1109/icirca.2018.8596817

Cited by 26 publications

(8 citation statements)

References 5 publications

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“…In their experiment, LR performed better than the rest and yielded the best classification metrics. In another work, Kumar and Katyal [120] reported that the C5.0 with adaptive boosting outperformed the classical classifiers like NB, RF, k-NN, actual C5.0 (a DT based classifier) and the k-means clustering algorithm in terms of accuracy, precision and recall values while detecting liver disease.…”

Section: Liver Disease Prediction Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Section: Liver Disease Prediction Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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“…The accuracy of classification and segmentation in detecting cysts was 90% and 80% respectively. Kumar and Katyal [7] briefs a method for analyzing LD using data mining techniques. In this research paper, they created a classifications model for diagnosis and to forecast liver problems using 5 data mining algorithms and 1 boosting algorithm.…”

Section:  Issn: 2302-9285mentioning

confidence: 99%

“…In this research paper, we use confusion metrics because of its best and easiest way to calculate the performance of a classification result that has two or more types of classes for output [20]. Using the matrices (TP, TN, FP, FN), the performance of the models is measured using (7) to (10). Table 1 and 2 shows the construct of the confusion matrix.…”

Section: Performance Measurementsmentioning

confidence: 99%

Supervised machine learning based liver disease prediction approach with LASSO feature selection

et al. 2021

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In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.

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“…This work presented a new Fuzzy-ANWKNN algorithm for the successful prediction of liver disorder. [20]. The main objective here was to accurately predict liver disorder by means of several data mining algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

The Diagnosis of Chronic Liver Disease using Machine Learning Techniques

Shaheamlung¹

2021

ITII

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In the 21st-century, the issue of liver disease has been increasing all over the world. As per the latest survey report, liver disease death toll has been rise approximately 2 million per year worldwide. The overall percentage of death by liver disease is 3.5% worldwide. Chronic Liver disease is also considered to be one of the deadly diseases, so early detection and treatment can recover the disease easily. Due to rapid advancement in Artificial intelligence (AI), like various machine learning algorithms SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc., This will improve the life span of a patient suffering from Chronic Liver Disease (CLD) in early stages. The data can be obtained in a large volume due to the broad exploitation of bar codes for supreme marketable products, the mechanization of various business and government dealings, and the development in the data collection tools. This research work is based on liver disease prediction using machine learning algorithms. Liver disease prediction has various levels of steps involved, pre-processing, feature extraction, and classification. In this s research work, a hybrid classification method is proposed for liver disease prediction. And Datasets are collected from the Kaggle database of Indian liver patient records. The proposed model achieved an accuracy of 77.58%. The proposed technique is implemented in Python with the Spyder tool and results are analyzed in terms of accuracy, precision, and recall.

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Effective Analysis and Diagnosis of Liver Disorder by Data Mining

Cited by 26 publications

References 5 publications

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Supervised machine learning based liver disease prediction approach with LASSO feature selection

The Diagnosis of Chronic Liver Disease using Machine Learning Techniques

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