Thyroid Disorder Analysis Using Random Forest Classifier

Mishra, Sushruta; Tadesse, Yeshihareg; Dash, Anuttam; Jena, Lambodar; Ranjan, Piyush

doi:10.1007/978-981-15-6202-0_39

Cited by 42 publications

(9 citation statements)

References 6 publications

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“…Mishra et al [ 18 ] applied the ML techniques sequential minimal optimization (SMO), DT, RF, and K-star classifier to predict hypothyroid disease. A sample size of unique 3772 records is considered for this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

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Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

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“…For mature markets such as the telecom industry, retention of old customers is vital in doing business. Customer segmentation, without it recommending the right product to the right customer at the right time, would be hard [146][147][148].…”

Section: Discussionmentioning

confidence: 99%

Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review

et al. 2021

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Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.

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“… Type ML Classifiers Brief Description Examples of healthcare predictions References Bagging ensemble Random Forest classifier Integrates bootstrap aggregation (bagging) and random feature selection to create a set of decision trees with controlled variation that can anticipate the corresponding output activity class [79] PCOS detection, lymph disease diagnosis, thyroid disorder analysis etc. Tiwari et al [80] , Azar et al [81] , Mishra et al [82] Boosting ensemble Gradient Boosting classifier It is an ensemble forward learning model which eliminates all weaker predictors in favor of a stronger one using an upgraded version of the decision tree, in which each successor is selected using the refined structure score, gain computation, and advanced approximations [83] Lung cancer detection, diabetes diagnosis, Leukemia prediction etc. Chandrasekar et al [84] , Bahad et al [85] , Deif et al [86] eXtreme Gradient (XG) Boosting This approach is scalable and efficient form of gradient boosting that improves on two fronts: tree construction speed and a novel distributed algorithm for tree searches [87] Heart disease detection, chronic kidney disease diagnosis, breast cancer detection etc.…”

Section: Methodsmentioning

confidence: 99%

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Suha¹,

Islam²

2023

Heliyon

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Thyroid Disorder Analysis Using Random Forest Classifier

Cited by 42 publications

References 6 publications

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

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