The standard IoT sensors and tools are to learn data construction techniques for creating a predictive model.The use of time series evaluation tools to identify thyroid tumors in their early stages is examined in this research. The records of thyroid ultrasound scans from 475 individuals are examined. The analysis is utilized to evaluate the predictor model's accuracy and the Time Series evaluation methodologies' suitability for correctly identifying thyroid cancer in its early stages. The results demonstrate the effectiveness of time-collection analytic techniques in the early detection of thyroid cancer. The results also highlight the potential for utilizing time series analytic techniques in various cancer-related early detection initiatives. The majority of thyroid tumors were found at an early stage using time series analysis, a finding that is the focus of this technical report. The program developed the ability to distinguish between benign and malignant tumors. The results of the observation demonstrated that the set of guidelines was effective in increasing the precision degree measurement using various wearable IoT Sensors. Additionally, the set of guidelines can identify the presence of a tumor before any scientific symptoms are apparent. The observer concluded that time-collecting analysis might be utilized to identify early cancer symptoms, which would undoubtedly lead to improved outcomes and more advanced treatments.
Keywords
Thyroid, Tumor, Detection, Cancer, Deep Learning, Early Stage, IoT, Sensors
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Cite this article
Tai hoon Kim, “Leveraging the Application of IoT based Deep Learning Prediction Model in Smart Healthcare”, Journal of Machine and Computing, pp. 083-093, January 2024. doi: 10.53759/7669/jmc202404009.
