Decision Stump and StackingC-Based Hybrid Algorithm for Healthcare Data Classification

Jha, Sunil Kumar; Paramasivam, Pavithara; Pan, Zhaoqing; Wang, Jinwei

doi:10.1007/978-3-030-00018-9_19

Cited by 3 publications

(2 citation statements)

References 34 publications

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“… Jha et al (2018) present a hybrid algorithm for healthcare data mining by using Decision Stump (DS), StackingC (SC), and voting methods to tackle the hyperthyroidism issue. In Imbus et al (2017) , a random tree and a rule-based classifier (JRip) are used to detect primary hyperparathyroidism; while in Pan et al (2016) , random forest, together with principal component analysis and rotation transformation, is used to detect a general thyroid disease by exploiting the UCI thyroid dataset.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

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Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Logistic Model Tree (LMT) [33] combines decision trees with logistic model trees. Decision Stump [34] is a onelevel decision tree. The method builds a decision tree with one internal node (the root) that is connected to its leaves.…”

Section: Decision Tree Methodsmentioning

confidence: 99%

COVID-19 Hastalarının Mortalitesini Tahmin Etmek için Torbalama ve Arttırma Yöntemleri

Arslan

2022

DÜMF MD

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COVID-19 pandemic has been going on for more than two years and an increasing number of deaths has been occurring. Ensemble learning techniques are effectively employed to predict the outcome of the patients with COVID-19. The mortality prediction of the COVID-19 patient is crucial to reduce the risk of imminent death as well as to apply effective clinical treatment strategy. In this study, we perform bagging and boosting methods to predict mortality of the patients with COVID-19. The six different decision tree methods, C4.5, Random tree, REPTree, Logistic Model Tree, Decision Stump, and Hoeffding Tree are employed for base learners in bagging and boosting. The results are obtained using a real-world dataset including information obtained from 1085 patients. Experimental results present that bagging using REPTree as a base learner achieves an accuracy of 97.24%. Furthermore, when we compare our results with other classification algorithms, the proposed method has a higher performance with respect to the accuracy, and presents an admirable performance.

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Multistep-ahead forecasting for maximum and minimum air temperatures using a new hybrid intelligence tree-based filter classifier

Shahdad

Saber

2022

Model. Earth Syst. Environ.

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Decision Stump and StackingC-Based Hybrid Algorithm for Healthcare Data Classification

Cited by 3 publications

References 34 publications

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A systematic review on artificial intelligence techniques for detecting thyroid diseases

COVID-19 Hastalarının Mortalitesini Tahmin Etmek için Torbalama ve Arttırma Yöntemleri

Multistep-ahead forecasting for maximum and minimum air temperatures using a new hybrid intelligence tree-based filter classifier

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