Designing a framework for diagnosing hepatitis disease using data mining techniques

Pushpalatha, S.; Pandya, Jagdish G.

doi:10.1109/icammaet.2017.8186708

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Pushpalatha et.al. [9] proposed a structure for diagnosing the Hepatitis disease. They clubbed Robust BoxCox Transformation (RBCT) and Neural Network (NN) models.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Classifier Accuracy in Liver Disease Diagnosis using a Novel Multi Layer Feed Forward Deep Neural Network

Murty¹,

Kumar²

2019

IJRTE

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 Abstract: Classification techniques are often used for predicting Liver diseases and assist doctors in early detection of liver diseases. As per studies in the past and our experiments, conventional classification algorithms are found to be less accurate in predicting liver diseases. Therefore, there is a need for sophisticated classifiers in this area. For many medical applications, including Liver Diseases, Deep Neural Networks (DNNs) are used but the accuracies are not satisfactory. Deep Neural Network training is a time taking procedure, particularly if the hidden layers and nodes are more. Most of the times it leads to over fitting and the classifier does not perform well on unseen data samples .We, in this paper, tuned a Multi Layer Feed Forward Deep Neural Network (MLFFDNN) by fitting appropriate number of hidden layer and nodes, dropout function after each hidden layer to avoid over fitting, loss functions, bias, learning rate and activation functions for more accurate liver disease predictions. We used a balanced data set containing 882 samples. The data is collected from north coastal districts of Andhra Pradesh hospitals, India. The training process is carried out for 400 epochs and finally It is .observed that our model exhibited 98% accuracy at epoch 363 which is more than the performance of Neural Network models tuned till now by machine learning researchers and also some regularly used classification algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision Tree, Random Belief Networks and Alternating Decision Trees (ADT) .

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“…Pushpalatha et.al. [9] proposed a structure for diagnosing the Hepatitis disease. They clubbed Robust BoxCox Transformation (RBCT) and Neural Network (NN) models.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Classifier Accuracy in Liver Disease Diagnosis using a Novel Multi Layer Feed Forward Deep Neural Network

Murty¹,

Kumar²

2019

IJRTE

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“…Several classification systems for chronic hepatitis [3,4] were suggested in the 1970s. There was a lot of work done in the 1990s to try to define and classify Chronic Hepatitis.…”

Section: Introductionmentioning

confidence: 99%

“…SE had the best accuracy (97%), followed by XGBoost (87%) and LR (85%). Doyle et al studied Quintiles and IQVIA's 2010-2016 dataset of approximately 10 million US HCV patients.All except [3] and [4] used data from the UCI mechanism knowledge source [6].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Hepatitis-Related Illnesses using Machine Learning

Venkatesan¹,

Abid²,

Abid³

2023

IJSMIEN

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Hepatitis is inflammation of the liver’s tissue and is often brought on by an infection. Several research efforts have been made to create machine learning algorithms for the diagnosis of hepatitis disease. However, the link between hepatitis and its symptoms is seldom discussed. The primary goal of this research was to describe in detail a dataset of hepatitis symptoms culled from actual cases. The study authors also suggested creating a standalone classification platform that uses random forest support vector machine algorithms and decision tree to differentiate between healthy and diseased individuals. To do this, we would choose applicable variables and enclose them in a malleable wrapper. Some traits have been proven to correlate highly with a hepatitis diagnosis. This article describes a technique that has the potential to enhance early-stage hepatitis detection, which might lessen the disease’s devastating impact on human life. Remember that of the three methods RF achieved the best accuracy and maintained its superiority across all datasets with very little variation.

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Predictive Analysis in Healthcare Using Feature Selection

Acharya

Patel

2021

Biomedical Data Mining for Information Retrieval

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Designing a framework for diagnosing hepatitis disease using data mining techniques

Cited by 5 publications

References 12 publications

Enhanced Classifier Accuracy in Liver Disease Diagnosis using a Novel Multi Layer Feed Forward Deep Neural Network

Enhanced Classifier Accuracy in Liver Disease Diagnosis using a Novel Multi Layer Feed Forward Deep Neural Network

Diagnosis of Hepatitis-Related Illnesses using Machine Learning

Predictive Analysis in Healthcare Using Feature Selection

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