Selected article for: "machine learning technique and random forest"
Author: Verde, Laura; De Pietro, Giuseppe; Sannino, Giovanna
Title: Artificial Intelligence Techniques for the Non-invasive Detection of COVID-19 Through the Analysis of Voice Signals
  • Cord-id: kpnkp19p
  • Document date: 2021_10_8
    • ID: kpnkp19p
    Snippet: Healthcare sensors represent a valid and non-invasive instrument to capture and analyse physiological data. Several vital signals, such as voice signals, can be acquired anytime and anywhere, achieved with the least possible discomfort to the patient thanks to the development of increasingly advanced devices. The integration of sensors with artificial intelligence techniques contributes to the realization of faster and easier solutions aimed at improving early diagnosis, personalized treatment,
    Document: Healthcare sensors represent a valid and non-invasive instrument to capture and analyse physiological data. Several vital signals, such as voice signals, can be acquired anytime and anywhere, achieved with the least possible discomfort to the patient thanks to the development of increasingly advanced devices. The integration of sensors with artificial intelligence techniques contributes to the realization of faster and easier solutions aimed at improving early diagnosis, personalized treatment, remote patient monitoring and better decision making, all tasks vital in a critical situation such as that of the COVID-19 pandemic. This paper presents a study about the possibility to support the early and non-invasive detection of COVID-19 through the analysis of voice signals by means of the main machine learning algorithms. If demonstrated, this detection capacity could be embedded in a powerful mobile screening application. To perform this important study, the Coswara dataset is considered. The aim of this investigation is not only to evaluate which machine learning technique best distinguishes a healthy voice from a pathological one, but also to identify which vowel sound is most seriously affected by COVID-19 and is, therefore, most reliable in detecting the pathology. The results show that Random Forest is the technique that classifies most accurately healthy and pathological voices. Moreover, the evaluation of the vowel /e/ allows the detection of the effects of COVID-19 on voice quality with a better accuracy than the other vowels.

    Search related documents:
    Co phrase search for related documents
    • accuracy f1 score achieve and machine learning: 1
    • accuracy f1 score and long lstm short term memory: 1, 2, 3, 4, 5
    • accuracy f1 score and lstm model: 1
    • accuracy f1 score and lstm short term memory: 1, 2, 3, 4, 5
    • accuracy f1 score and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55
    • accuracy f1 score and machine learning ml algorithm: 1
    • accuracy f1 score and machine perform: 1
    • accurate analysis and long lstm short term memory: 1, 2
    • accurate analysis and lstm short term memory: 1, 2
    • accurate analysis and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
    • accurate healthcare and live environment: 1
    • accurate healthcare and machine learning: 1, 2, 3, 4