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.
翻译:全球冠状病毒大流行使许多保健系统不堪重负,强制封锁和鼓励在家工作,以控制病毒的传播,防止住院病人过多出院。这促使人们大量使用远程保健,为病人提供低风险护理。然而,不断变异为新的变异,测试包普遍缺乏,特别是在发展中国家,因此难以控制未来潜在的感染浪潮。在本文中,我们提议建立一个新型智能手机应用平台,用于早期诊断可能的Covid-19受感染病人。应用程序提供了三种诊断模式,从可能的症状、咳嗽声和特定血液生物标志中进行广泛的诊断。当用户选择特定设置并提供必要的信息时,它将数据发送到远程服务器上部署的经过培训的机器学习模式(ML)中。ML算法随后预测了与Covid-19接触的可能性,并将反馈发送给用户。我们的机器学习模型可以确定Covid-19患者的准确度为100%,95.65%,从血液参数、咳嗽和症状的77.59 %。此外,ML对远程诊断方法的精确度和精确度的精确度分别显示ML的准确性,这是ML的精确度,这是ML的精确度,而精确性分析方法的精确性为C的准确性,这在C中可以使ML的准确性分析方法的精确性,是精确性,是精确性。