International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume:10 Issue:6 | June 2023
p-ISSN: 2395-0072
www.irjet.net
Angina Pectoris Predicting System using Embedded Systems and Big Data Dangeti Sai Chaithrik, Chiluka Harshith Reddy Dangeti Sai Chaithrik, Dept. of Electronics and communication Engineering, VIT university, Tamil Nadu, India Chiluka Harshith Reddy Dept. of Electronics and communication Engineering, VIT university, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------information [1]. Establishing IOT environment with Abstract - The proposed system paves a way to predict the
wearable sensor devices like Heart rate senor, ECG for the data collection and the collected data is sent to the Arduino for processing of data [2]. The processed data is sent into the cloud like Thingspeak for continuous visualization and to get a true picture of the patient’s heart rate and temperature variations however this paper does not propose a way to integrate the ECG graph into the Thingspeak [3]. The pulse rate is divided into three categories namely low heart rate where BPM<60, Normal heart rate where BPM lies between 60 to 100 and high pulse rate where BPM>100 these variations can better help to visualize the data and these classifications can be done in the Arduino initialization [4]. It is crucial to create portable heart detection systems that can process signals in real time and analyse an ECG in order to keep track of high-risk patients and help doctors make judgements [5]. Unlike the unsupervised learning the supervised learning the algorithm is trained on labelled data that includes both input and output features and in turn give better test accuracy [6]. logistic regression predicts heart disease by calculating the likelihood of the outcome variable (heart disease) based on a collection of predictor factors using a logistic function (sigmoid curve) [7]. The Random Forest technique builds many decision trees, each of which is trained using a different random subset of the data. Each tree in the forest predicts whether a certain patient has heart disease or not, and the combined projections of all the trees in the forest yield the final result [8]. The K-NN algorithm looks for the "k" closest patients in the training data based on the similarity of their features to the new patient's features and the "k" closest patients are then used to make a prediction about whether the new patient is likely to have heart disease or chest pain [9]. The linear SVM algorithm works by finding the best hyperplane that separates the data into different classes. In the case of heart disease prediction, the algorithm tries to find the best hyperplane that separates the patients who have heart disease from those who don't [10]. Naive Bayes works by using Bayes' theorem, which is a statistical formula that calculates the probability of an event occurring based on prior knowledge of conditions that might be related to the event. In the case of heart disease prediction, the algorithm tries to calculate the probability that a patient has heart disease [11]. K-NN is a nonparametric algorithm, which means that it does not make any assumptions about the underlying distribution of the data. This allows the algorithm to be highly flexible and adapt well to different types of data [12]. To lessen
disease with the seamless integration of both hardware and Machine learning. The embedded System consists of components like heart rate sensor, SpO2 sensor, ESP8266 WIFI-module and ECG sensor for collecting the patient data modules that includes patient heart rate, rest ECG, temperature and SpO2 and are visualized using Thing Speak cloud. The heart rate ranges from 20BPM to 150BPM where BPM above 82 is considered as high or abnormal heart rate and BPM below 60 is considered as low heart rate. After collection this data is fed into the five machine learning algorithms which includes 1. logistic regression 2. Random Forest 3. K-NN 4. Linear SVM 5. Naive bayes for training and testing the data. The efficiency is highest for K-NN with 0.8703 and Random Forest with 0.9640 test accuracy respectively so the proposed system uses these two models for the prediction. In the web page both K-NN and Random Forest are incorporated to predict if the patient is suffering from disease or not from various field ID’s which are inputted by the end user. Key Words: Anaconda, BeatsPerMinute (BPM), Classifier, Electrocardiography (ECG), prediction rate, Thingspeak.
1.INTRODUCTION IoT refers to the network of physical devices, sensors, and connectivity that enable these objects to connect and exchange data. In the healthcare industry, IoT devices can be used to monitor patients in real-time, collect health data, and provide remote care. ML, on the other hand, that enables machines to learn from data and make predictions based on that data. In the healthcare industry, ML can be used to analyse vast amounts of patient data to identify patterns, predict outcomes, and make personalized treatment recommendations. The combination of IoT and ML has the potential to revolutionize the healthcare industry and improve the lives of patients and healthcare providers. The literature reported in [1-17] deals with various ways to collect various health parameters like heart rate, NodeMCU, ECG of the patients and the application of various machine learning models in training and testing the acquired data. Health monitoring using sensors and predicting framework using IOT portrays the intercorrelation and assortment of the patient health
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