Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

Kumar, Pushpendra; Thakur, Ramjeevan Singh

doi:10.1007/s11042-019-07978-3

Cited by 33 publications

(12 citation statements)

References 41 publications

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“…It is observed that the decision algorithm has the highest precision, recall and f1-score as well with 92%, 99% and 95.3% respectively. [22] 2019 ILPD LG 72.50 % Thaiparnit et al [23] 2018 Liver Disorder RF 75.76 % Rahman et al [24] 2019 ILPD LG 75% Kumar and Thakur [25] 2020 BUPA, ILPD Fuzzy-NWKNN 78.46% Rabbi et al [26] 2020 ILPD AdaBoost 92.19% Poonguzharselvi et al [27] 2021 UCI repository Random Forest 84%…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…Furthermore, the BiLSTM captures all semantic features and can generate decent writing of the remarks. is research paper compared its proposed BiLSTM architecture with existing Naive Bayesian, CNN [31], RNN, and LSTM [32]. Lastly, the opinion characteristic of a message is determined using a co-evolutionary network with SoftMax maps.…”

Section: Related Researchmentioning

confidence: 99%

“…CC: calibrated classifier[26][27][28], VC: voting classifier, SVC: support vector classifier, DT: decision tree, ANNs: artificial neural networks[29], RNN: recurrent neural network, LSTM: long short-term memory, GNB: Gaussian naive Bayes, K-NN: K-nearest neighbor[30][31][32], ETC: extra trees classifier, NB: naïve Bayes, GBM: gradient boosting machine, RF: random forest, LR: logistic regression, and SGD: stochastic gradient descent.…”

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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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“…Kumar and Thakur (P. Kumar & Thakur, 2020) presented a method that aims to improve the performance of the imponderable's liver disease data classification. variable neighbor weighted fuzzy KNN approach (Variable-NWFKNN) was proposed to be used instead of using the Fuzzy KNN classifier because it`s not working well on the imbalanced dataset due to its neighbor equally.…”

Section: Indian Liver Diseasementioning

confidence: 99%

Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

Al-Hashem

Alqudah

Qananwah

2021

International Journal of E-Health and Medical Communications

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Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

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Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

Cited by 33 publications

References 41 publications

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

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

Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model

Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

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