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| | Download PDFOpen PDF in browser Download PDFOpen PDF in browserThyroid Disease Detection Using Machine Learning ApproachEasyChair Preprint 106848 pages•Date: August 7, 2023AbstractThyroid disorders are prevalent worldwide andcan significantly impact an individual's health and well
 being. The accurate detection and diagnosis of thyroid
 diseases are crucial for effective management and treatment.
 The most common thyroid
 hypothyroidism. Hypo- means deficient or under (active), so
 hypothyroidism is a condition in which the thyroid gland is
 underperforming or producing too little thyroid hormone.
 Recognizing the symptoms of hypothyroidism is extremely
 important. The proposed program leverages a diverse
 a dataset comprising various thyroid-related parameters,
 including patient demographics, medical history and
 laboratory test results. By harnessing the power of machine
 learning algorithms, the program learns intricate
 and predicts accuracy accordingly. The program employs
 several machine learning techniques to build a robust and
 reliable thyroid disease detection model, including feature
 extraction, feature selection, and classification algorithms
 We take the assistance of RandomForestClassifier and
 StandardScaler Through an iterative training process, the
 the program optimizes the model's performance by minimizing
 false positives and false negatives, ensuring accurate
 predictions and reducing the likelihood of mi
 program's performance is compared against existing
 diagnostic methods, including clinical guidelines and expert
 interpretations of medical professionals, to validate its
 efficacy and potential for clinical adoption.The results of the
 the evaluation demonstrates that the machine learning
 thyroid detection program achieves superior performance in
 terms of accuracy and efficiency compared to traditional
 diagnostic approaches.
 Keyphrases: Hypothyroidism, RandomForestClassifier, Thyroid, extraction | 
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