Application of a Mobile Chronic Disease Health-Care System for Hypertension Based on Big Data Platforms

Abstract

Hadoop is a globally famous framework for big data processing. Data mining (DM) is the key technique for the discovery of the useful information from massive datasets. In our work, we take advantage of both platforms to design a real-time and intelligent mobile health-care system for chronic disease detection based on IoT device data, government-provided public data and user input data. The purpose of our work is the provision of a practical assistant system for self-based patient health care, as well as the design of a complementary system for patient disease diagnosis. This system was only applied to hypertensive disease during the first research stage. Nevertheless, a detailed design, an implementation, a clear overview of the whole system, and a significant guide for further work are provided; the entire step-by-step procedure is depicted. The experiment results show a relatively high accuracy.

Somewhat Homomorphic Encryption: Ring Learning with Error Algorithm for Faster Encryption of IoT Sensor Signal-Based Edge Devices

“…As per the World Health Organization (WHO) report, it increases the mortality rate. Hypertension [9] is one of the chronic conditions in which blood pressure increases in the arteries also called High Blood Pressure (HBP). HBP does not usually cause symptoms, but it leads to a major risk influence in heart failure, kidney failure, heart attack, artery disease, and so on.…”

Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

“…The prevalence of asthma is at a higher rate in elderly men living in urban areas as compared to elderly women and rural areas. Many studies showed that stress can also worsen asthma [25,26]. Heart diseases include heart attack, stroke, cardiac arrest, high blood pressure, peripheral artery disease, cardiovascular condition, and heart failure.…”

“…It is very important to have proper verification and testing techniques to deal with the different critical domains. Pradeebha et al and Bhuvaneswari et al [26,39] addressed the feature extraction of unstructured data, in the form of images using various filtering techniques, such as the Gabor Kernel Filter (GKF), Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). Bhuvaneswari et al [39] described the feature selection, which was carried out by various methods like information gain, Correlation-based Feature Selection (CFS), Principal Component Analysis (PCA), Genetic Algorithm (GA) and fuzzy c-means clustering.…”

“…The study did not focus on any specific health care problem. Pradeebha et al [26] employed SVM Radial Basis Function as classifier and obtained an accuracy of 98%. Li et al [27] demonstrated the classification model using C4.5 and SVM methods.…”