Diagnosis of Liver Disorder Using Fuzzy Adaptive and Neighbor Weighted K-NN Method for LFT Imbalanced Data

Kumar, Pushpendra; Thakur, Ramjeevan Singh

doi:10.1109/icsss.2019.8882861

Cited by 20 publications

(8 citation statements)

References 11 publications

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“…e intrinsic shortcoming of bag-of-words data analysis is that the LSA classifier does not evaluate sentence-level certain text content deriving from a grammatical structure. Xu et al [30] suggested a BiLSTM-based sentiments data analysis for posts and used it to address the problem of post-sentiment classification.…”

Section: Related Researchmentioning

confidence: 99%

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Mahto

Yadav

Lalotra³

2022

Mobile Information Systems

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With the emergence of social media platforms, most people have changed their way of interacting. Perhaps, sharing day-to-day lifestyle updates is a trend substantially influenced by microblogging sites, specifically Twitter, Facebook, Instagram, and many more. Moreover, text and messages are the most preferred way for such interactions. Twitter is one of the most commonly used microblogging tools that enable people to express their thoughts, opinions, emotions, happiness, sadness, excitement, ideas, mental stress, and so on. Hence, the sentiment prediction furnished by such textual data becomes a complex and challenging task. In this research, the authors proposed a hybridization of the convolutional neural network and bi-directional long short-term memory model (named ConvBidirectional-LSTM), which aims to better the categorization of sentiments of text data. Then, this proposed hybrid ConvBidirectional-LSTM model is compared with the existing state-of-the-art models, GloVe-based CNN-LSTM and Hierarchical Bi-LSTM (HeBiLSTM) models models. Furthermore, the performance of the proposed hybrid ConvBidirectional-LSTM model is evaluated on the US airline dataset using various performance parameters like accuracy, precision, recall, and f 1 score. The proposed model outperformed the existing state-of-the-art models with an accuracy rate of 93.25% in sentiment prediction.

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Section: Related Researchmentioning

confidence: 99%

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Mahto

Yadav

Lalotra³

2022

Mobile Information Systems

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“…For the liver function test, the fuzzy adaptive and neighbor weighted k‐NN (FuzzyANWKNN) method was introduced by Kumar et al 15 . This FuzzyANWKNN demonstrated better prediction results than MPRLPD and ILPD imbalanced dataset.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…The general reason for prominent serum alanine aminotransferase (ALT) is a nonalcoholic fatty liver disease (NAFLD) in which the concentration of normal serum liver enzyme is present in a NAFLD 11–13 . From the voluminous medical databases, the researchers face several demanding tasks in the healthcare section for disease prediction 13–20 . In medical diagnosis, the most popular model such as machine learning or artificial intelligence‐based classification techniques is used as classification techniques 21–23 …”

Section: Introductionmentioning

confidence: 99%

ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification

Padmakala

Subasini

Karuppiah

et al. 2021

Numer Methods Biomed Eng

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Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM‐based sample weighted random forests (eSVM‐swRF) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre‐process the patient data. The significant feature with a suitable model is generated depending upon the filter‐based method. Based on eSVM‐swRF, the parameter values such as penalty parameter (P), threshold (T), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM‐swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization‐based Support Vector Machine (PSO‐SVM), fuzzy adaptive, and neighbor weighted k‐NN (FuzzyANWKNN), Naïve Bayes‐based Support Vector Machine (NB‐SVM), and Neural network.

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“…This work made use of standard Indian liver illness patient records as a database for providing support to the researcher. Pushpendra Kumar, et.al (2019) stated that it was a very difficult task for the doctors to detect the consequences of liver disorders on a person [19]. In general, researchers used datasets based on LFT (Liver Function Test) for implementing classification algorithms so that the predictions about liver disorders could be generated.…”

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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Diagnosis of Liver Disorder Using Fuzzy Adaptive and Neighbor Weighted K-NN Method for LFT Imbalanced Data

Cited by 20 publications

References 11 publications

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification

The Diagnosis of Chronic Liver Disease using Machine Learning Techniques

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