k-gokulappadurai/asthma-risk-predicition-using-machine-learning-and-internet-of-things

An Asthma risk predictor using machine learning by the help of dataset

Asthma-Predicition-Using-Machine-Learning-and-Internet-of-Things

ABSTRACT 

            In this project, we present an asthma risk prediction tool based on machine learning. The entire tool is implemented on a smartphone as a mobile-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 asthma risk predictors. In this work, we find a correlation between the particulate matter found indoors and the outside weather with the PEFR. Convolutional neural network 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-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 surveys. 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.

OBJECTIVE

       In this section, we discuss the proposed asthma risk prediction method. The data and the deep learning network used for the model training are discussed. The block diagram of the proposed system is shown. The weather data and the indoor air pollution characterized by PM2.5 and PM10 data are the input to the Deep learning model and the peak expiratory flow rate (PEFR) provides the labels used in training the model. we discuss the tools and the steps involved in IoT implementation. The real-time data collection, the utilization of the data and the smartphone app are explained in this section.

ALGORITHM :

Algorithm explaining the proposed system working in real-time Input: PM2.5, PM10, outdoor temperature, humidity. Output: Safe, Moderate or High asthma risk prediction. Data processing stage on the Raspberry Pi: Collect PM2.5, PM10 using SDS011; Collect weather data using Openweather map; Data hosting the input features to server; Real-time stage on the Smartphone:

while App ON 
	 do Collect data from Web;
	 CNN prediction;
	 if PEFR > 80% then 
		Safe; 
	else if 50% < PEFR < 80% then 
		Moderate risk;
	 else 
		High risk;
	 end
end

WORKING

The patient want to take the best PEFR value The application deployed in the edge device is installed with the model we trained by the asthma patients The inputs are give to the model using the text field The result calculated and the status will be displayed By the methodology 1 we will fetch the air quality data using sensors which is connected to raspberry pi By the methodology 2 we will fetch the air quality data using the online weather API called OPENWEATHER. As per the resulted PEFR value the result will any of SAFE, MODERATE, RISK.

GRAPHICAL USER INTERFACE

Application using python KIVY Frame work With sensor input manually given method & Without sensor inputs and using openweather api call by the geological coordinates

Output for the both methods

Usage

To run the application locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/K-GOKULAPPADURAI/Asthma-Risk-Predicition-Using-Machine-Learning-and-Internet-of-Things

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Kivy application with sensor inputs:

   python main.py

4. For Running the Kivy application with location as input:

   python main1.py

CONCLUSION

In this project, we presented an asthma risk prediction tool based on a convolutional neural network. The PEFR readings are predicted using simple PM and weather data. The performance improvement of the proposed method is observed using objective evaluations. This cost-effective tool involves an edge device, sensors and an IoT platform. The entire tool is implemented on a smartphone as application using several resources of IoT. The tool can be successfully used to predict asthma risk of individual patients.

CONTACT

Feel free to ping me

phone : [phone] gmail : [email protected]