International Journal of Internet, Broadcasting and Communication

  • 제16권3호
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  • Pages.321-328
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  • 2024
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  • 2288-4920(pISSN)
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  • 2288-4939(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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Design of Disease Prediction Algorithm Applying Machine Learning Time Series Prediction

  • Hye-Kyeong Ko (Department of Computer Engineering, Sungkyul University)
  • 투고 : 2024.07.07
  • 심사 : 2024.07.19
  • 발행 : 2024.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

This paper designs a disease prediction algorithm to diagnose migraine among the types of diseases in advance by learning algorithms using machine learning-based time series analysis. This study utilizes patient data statistics, such as electroencephalogram activity, to design a prediction algorithm to determine the onset signals of migraine symptoms, so that patients can efficiently predict and manage their disease. The results of the study evaluate how accurate the proposed prediction algorithm is in predicting migraine and how quickly it can predict the onset of migraine for disease prevention purposes. In this paper, a machine learning algorithm is used to analyze time series of data indicators used for migraine identification. We designed an algorithm that can efficiently predict and manage patients' diseases by quickly determining the onset signaling symptoms of disease development using existing patient data as input. The experimental results show that the proposed prediction algorithm can accurately predict the occurrence of migraine using machine learning algorithms.

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과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT). (NO.NRF-2021R1A2C1012827) in (2023)

참고문헌

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