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Download PDFOpen PDF in browserThyroid Disease Detection Using Machine Learning ApproachEasyChair Preprint 106848 pages•Date: August 7, 2023AbstractThyroid disorders are prevalent worldwide and can 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 Download PDFOpen PDF in browser |
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