Telehealthcare and Covid-19: A Noninvasive & Low Cost Invasive, Scalable and Multimodal Real-Time Smartphone Application for Early Diagnosis of SARS-CoV-2 Infection

• URL: http://arxiv.org/abs/2109.07846v1
• Date: Thu, 16 Sep 2021 10:22:31 GMT
• Title: Telehealthcare and Covid-19: A Noninvasive & Low Cost Invasive, Scalable and Multimodal Real-Time Smartphone Application for Early Diagnosis of SARS-CoV-2 Infection
• Authors: Abdullah Bin Shams, Md. Mohsin Sarker Raihan, Md. Mohi Uddin Khan, Rahat Bin Preo and Ocean Monjur
• Abstract summary: We propose a novel Smartphone application-based platform for early diagnosis of possible Covid-19 infected patients. The application provides three modes of diagnosis from possible symptoms, cough sound, and specific blood biomarkers. Our machine learning models can identify Covid-19 patients with an accuracy of 100%, 95.65%, and 77.59% from blood parameters, cough sound, and symptoms respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The global coronavirus pandemic overwhelmed many health care systems, enforcing lockdown and encouraged work from home to control the spread of the virus and prevent overrunning of hospitalized patients. This prompted a sharp widespread use of telehealth to provide low-risk care for patients. Nevertheless, a continuous mutation into new variants and widespread unavailability of test kits, especially in developing countries, possess the challenge to control future potential waves of infection. In this paper, we propose a novel Smartphone application-based platform for early diagnosis of possible Covid-19 infected patients. The application provides three modes of diagnosis from possible symptoms, cough sound, and specific blood biomarkers. When a user chooses a particular setting and provides the necessary information, it sends the data to a trained machine learning (ML) model deployed in a remote server using the internet. The ML algorithm then predicts the possibility of contracting Covid-19 and sends the feedback to the user. The entire procedure takes place in real-time. Our machine learning models can identify Covid-19 patients with an accuracy of 100%, 95.65%, and 77.59% from blood parameters, cough sound, and symptoms respectively. Moreover, the ML sensitivity for blood and sound is 100%, which indicates correct identification of Covid positive patients. This is significant in limiting the spread of the virus. The multimodality offers multiplex diagnostic methods to better classify possible infectees and together with the instantaneous nature of our technique, demonstrates the power of telehealthcare as an easy and widespread low-cost scalable diagnostic solution for future pandemics.

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