Dingkun Li

Design and Partial Implementation of Health Care System for Disease Detection and Behavior Analysis by Using DM Techniques

Data Mining (DM) techniques such as classification, clustering, association, regression etc. are widely used in healthcare field in recent years to help improve the quality, efficiency as well as lowering the cost of developing healthcare systems. Especially, with the rapid development of the cloud platform services, which not only reduces the cost (time and expenditure), but also breaks the boundaries of data transactions among different systems and users. Therefore, it provides an effective way to reduce the time, and cost of software development as well as up-to-date services. In our work, we designed and partially implemented a healthcare system based on cloud services for disease detection and prediction using DM techniques in order to provide better services for both patients and health care givers. Although the system has been partially implemented, the experimental results are encouraging.

The Design and Partial Implementation of the Dementia-Aid Monitoring System Based on Sensor Network and Cloud Computing Platform

Sensor networks integrated with cloud computing platform provides a promising way to develop monitoring system for elderly people especially dementia patients who need particular care for their normal life. In our work, we aim to design a comprehensive, unobtrusive, real-time, low-cost but effective monitoring system to help caregivers for their daily healthcare work. The system design has been finished and the entire experimental environment including sensor network and cloud model has been set up in our lab to collect simulated data and one health care center to collect real data. Though the system has been partially implemented due to time limit, the experiment results are encouraging.

Risk factors rule mining in hypertension: Korean National Health and Nutrient Examinations Survey 2007–2014

The prevention of hypertension is one of the most important topics in health research. In the most of the previous studies used statistical methods for analyzing the association between hypertension prevalence and dietary. However, statistical methods have some limitation which are, it is difficult to interpret variables interaction at a time. Thus we apply the data mining techniques for generation of prognosis factors based on association rule mining. In our experiment, we conducted Korean National Health and Nutrient Examination Survey (KNHANES) data from 2007 to 2014. We used to filter-based feature selection method for find prognosis factors and we generate the rules based on discovered risk factors of prognosis in hypertension. We evaluated discovered rules by support and confidence. In the results shows that, we can find useful rules for prognosis of hypertension. We expected to support medical decision making and easy to interpret prognosis of hypertension.

DeepEnergy: Prediction of Appliances Energy with Long-Short Term Memory Recurrent Neural Network

Our world is becoming more interconnected and intelligent, huge amount of data has been generated newly. Home appliances’ energy usage is the basis of home energy management and highly depends on weather condition and environment. Using weather in context, it is theorized that usage of home energy would be higher in cold days. Time series and contextual data collected from sensors can be monitored and controlled in home appliances network. The aim of this work is to propose a deep neural network architecture and apply it to a contextual and multivariate time series data. Long short-term memory (LSTM) models are powerful neural networks based on past behaviours in long sequences. LSTM networks have been demonstrated to be particularly useful for learning sequences containing longer-term patterns of unknown length, due to their ability to maintain long-term memory. In this work, we incorporate contextual features into the LSTM model because of ability of keeping context of data for a long-time, and for analysing it we integrated two different datasets; the first dataset contains measurements about house temperature and humidity measured over a period of 4.5 months by a 10 min intervals using a ZigBee wireless sensor network. The second dataset contains measurements about individual household electric power consumptions gathered over a period of 47 months. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction.

A Simply Way for Chronic Disease Prediction and Detection Result Visualization

Disease data provide an abundant source for chronic disease research. Hundreds of applications have been developed to deliver healthcare based on this big data. However, very few applications provide efficient chronic disease data visualization methods to better understand the results. This paper introduces a simple and practical way for visualizing the results of chronic disease detection and prediction. A model called IVIS4BigData has been used to implement the visualization procedure. This model not only demonstrates the historical data but also provides state-of-the-art visualization techniques. An exemplary set of scenarios corresponding to system design as well as visualization evaluation are given at last. Also we consulted several domain experts and common users about our visualization experimental results which satisfied their understanding about our systems. Finally conclusion and overlook of future work complete the paper.

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

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.

A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis

Recently, various studies have shown that meaningful knowledge can be discovered by applying data mining techniques in medical applications, i.e., decision support systems for disease diagnosis. However, there are still several computational challenges due to the high-dimensionality of medical data. Feature selection is an essential pre-processing procedure in data mining to identify relevant feature subset for classification. In this study, we proposed a hybrid feature selection mechanism by combining symmetrical uncertainty and Bayesian network. As a case study, we applied our proposed method to the hypertension diagnosis problem. The results showed that our method can improve the classification performance and outperformed existing feature selection techniques.

Design of SaaS based health care system for disease detection and prediction

Data Mining (DM) techniques such as classification, clustering, association, regression etc. provide a promising way to help improving the quality, efficiency and lowing the cost of developing the healthcare systems. Especially with the rapid development of the cloud platform services, like SaaS, it does not only reduce the cost (time and expenditure) but also breaks the boundaries of the data transaction among different systems and users. In this work, we briefly described a healthcare system based on SaaS services for disease detection and prediction by using DM techniques so as to provide better service for both patients and health care givers. Promisingly, our work will provide a guideline for the next stage of research.