Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

Abstract

In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external instruments such as peak flow meters and are well known asthama risk predictors. In this work, we find a correlation between the particulate matter (PM) found indoors and the outside weather with the PEFR. The PEFR results are classified into three categories such as 'Green' (Safe), 'Yellow' (Moderate Risk) and 'Red' (High Risk) conditions in comparison to the best peak flow value obtained by each individual. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the PEFR values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. These performance measures are better for the proposed method than other methods discussed in the literature. The entire setup is implemented on a smartphone as an app. An IoT system including a Raspberry Pi is used to collect the input data. This assistive tool can be a cost-effective tool for predicting the risk of asthma attacks.

Real-Time Low-Cost Personal Monitoring for Exposure to PM2.5 among Asthmatic Children: Opportunities and Challenges

“…Aside from the efforts to finetune the existing mobile sensors and devices, dedicated efforts should be made to develop GPS-enabled wearable and patchable devices as a next-generation environmental and health monitoring tool that assesses the true level of environmental exposure to the human body at every second or minute interval [41,42]. The recent development of IoT technology and deep learning algorithm could serve as promising tool for processing and analyzing the real-time air pollution data [43,44] that underpins the backbone of evidence-based personalized medicine and environmental exposure management.…”

AI Powered Asthma Prediction Towards Treatment Formulation: An Android App Approach

“…The imputation will initially be done to adapt the missing data, followed by the feature scaling to standardize the dataset's value range. Finally, the binary classifiers are fitted to the data during the k-fold cross validation phase with all the samples for training, validation, and testing to provide a more robust classification [13] following that, the machine learning file is loaded into Android Studio. We generated a file using Tensorflow after receiving predictions from ML models, and this file was imported into Android Studio.…”

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When explainable AI meets IoT applications for supervised learning

“…The novel system consists of the study of the IoT variation and the different kinds of symptoms for real COVID cases. Bhat et al [5] studied the correlation between indoor and the outdoor sensors to predict the Asthma risk from IoT sensor data. The convolutional neural network is trained to learn the matching among both the indoor observation, the outdoor observation, and the prediction values.…”

“…Algorithm 1 presents the pseudo-code of the developed XDIoT algorithm. The process starts by transforming the sensor data into an image database (lines [4][5][6][7][8][9][10][11]. We scan all sensor data, for each value in the given sensor, we associate its value to 1, in its corresponding image.…”