A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 7; pp. 1931 - 1 - 1931-20
• Main Authors: Harb, Hassan, Mroue, Hussein, Mansour, Ali, Nasser, Abbass, Motta Cruz, Eduardo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.03.2020; MDPI
• Series: Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth
• Subjects: Algorithms; Archives & records; association mining rules; Big Data; Biosensing Techniques; Biosensors; Blood pressure; Classification; Computer Science; Data analysis; Data collection; Decision Making; Decision making models; Delivery of Health Care; Diagnostic Techniques and Procedures; Digital signatures; disease diagnosis; Engineering Sciences; Fuzzy sets; hadoop platform; healthcare applications; Heart rate; Hospitals; Humans; Information Storage and Retrieval; Internet of Things; Medical diagnosis; Monitoring, Physiologic; Multiagent Systems; patient classification; Patients; Patients - classification; Remote sensing; Remote Sensing Technology; Respiration; Sensors; SK-means; Variance analysis; Vital signs; Wireless networks; patient classification; disease diagnosis; association mining rules; healthcare applications; hadoop platform; SK-means
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20071931

Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients’ classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.