Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: Simulated dataset accompanied with codes

Abstract

This paper provides simulated datasets for triaging and prioritizing patients that are essentially required to support multi emergency levels. To this end, four types of input signals are presented, namely, electrocardiogram (ECG), blood pressure, and oxygen saturation (SpO2), where the latter is text. To obtain the aforementioned signals, the PhysioNet online library [1] , is used, which is considered as one of the most reliable and relevant libraries in the healthcare services and bioinformatics sciences. In particular, this library contains collections of several databases and signals, where some of these signals are related to ECG, blood pressure, and SpO2 sensor. The simulated datasets, which are accompanied by codes, are presented in this paper. The contributions of our work, which are related to the presented dataset, can be summarized as follow. (1) The presented dataset is considered as an essential feature that is extracted from the signal records. Specifically, the dataset includes medical vital features such as: QRS width; ST elevation; peaks number; cycle interval from ECG signal; SpO2 level from SpO2 signal; high blood (systolic) pressure value; and low-pressure (diastolic) value from blood pressure signal. These essential features have been extracted based on our machine learning algorithms. In addition, new medical features are added based on medical doctors' recommendations, which are given as text-inputs, e.g., chest pain, shortness of breath, palpitation, and whether the patient at rest or not. All these features are considered to be significant symptoms for many diseases such as: heart attack or stroke; sleep apnea; heart failure; arrhythmia; and blood pressure chronic diseases. (2) The formulated dataset is considered in the doctor diagnostic procedures for identifying the patients' emergency level. (3) In the PhysioNet online library [1] , the ECG, blood pressure, and SpO2 have been represented as signals. In contrast, we use some signal processing techniques to re-present the dataset by numeric values, which enable us to extract the essential features of the dataset in Excel sheet representations. (4) The dataset is re-organized and re-formatted to be presented in a useful structure feasible format. Specifically, the dataset is re-presented in terms of tables to illustrate the patient's profile and the type of diseases. (5) The presented dataset is utilized in the evaluation of medical monitoring and healthcare provisioning systems [2] . (6) Some simulated codes for feature extractions are also provided in this paper.

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

“…This section provides a comprehensive description of data set construction, development models, and disease prediction, from the data collected to the outcome of the suggested methods. First, the disease dataset was obtained from the reliable dataset in the symptoms format [18]. Afterwards, the dataset underwent data pre-processing, including feature selection, data digitization, and feature scaling (normalization).…”

“…This study implemented supervised ML algorithms, which include Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), based on Hadoop and Spark environments for predicting two chronic disease types, such as heart disease and hypertension. The data set is upgraded from [18] to get 55,680 patient records. The data set in [18] comprises 11 features and 580 records.…”

“…The data set is upgraded from [18] to get 55,680 patient records. The data set in [18] comprises 11 features and 580 records. However, our study, on the other hand, has utilized 13 features with 55,680 records using Hadoop and Spark environments.…”

“…The available data for the Heart Disease Set consists of 11 features [18], which were considered for testing purposes in this study. However, all related experiments utilized 500 sets; with each set containing information about the features of one patient.…”

The software requirements process for designing a microcontroller-based voice-controlled system

“…The command is provided through Bluetooth-enabled mobile phone and translated into a string using Arduino's BT voice control before being transmitted to the SR-04 Bluetooth module or wheelchair control Arduino board. This strategy saves money and time for physically challenged patients who use wheelchairs [20]- [22]. The recommended solution makes it easier to operate equipment in multiple rooms from a single place.…”