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Thyroid Disease Classification Using Machine Learning Algorithms

Salman Khalid, Emrullah Sonuç

2021Journal of Physics Conference Series109 citationsDOIOpen Access PDF

Abstract

Abstract With the vast amount of data and information difficult to deal with, especially in the health system, machine learning algorithms and data mining techniques have an important role in dealing with data. In our study, we used machine learning algorithms with thyroid disease. The goal of this study is to categorize thyroid disease into three categories: hyperthyroidism, hypothyroidism, and normal, so we worked on this study using data from Iraqi people, some of whom have an overactive thyroid gland and others who have hypothyroidism, so we used all of the algorithms. Support vector machines, random forest, decision tree, naïve bayes, logistic regression, k-nearest neighbors, multi-layer perceptron (MLP), linear discriminant analysis. To classification of thyroid disease.

Topics & Concepts

Machine learningArtificial intelligenceNaive Bayes classifierComputer scienceDecision treeSupport vector machineThyroidAlgorithmPerceptronRandom forestStatistical classificationThyroid diseaseCategorizationLogistic regressionLinear discriminant analysisDiseaseMedicineEndocrinologyInternal medicineArtificial neural networkArtificial Intelligence in HealthcareData Mining Algorithms and Applications
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