Energy Distance based Rank Driven Patients Data Routing and Bayesian Network Disease Classification Model for Telemedicine

Abstract

Telemedicine system can track vital signs of patients with chronic disease and electronically newlinetransmits the data to healthcare providers without waiting for emergency to occur. newlineThis helps in providing an alert system that assist physicians to diagnose problems and newlineget treatment to patients in proactive manner. This capability will be achieved with high newlinebandwidth networks and devices, to deliver real-time analytics that can enhance health newlineoutcomes. In general, rural patients are unable to reach multispeciality expert doctor for newlinediagnosis and treatment for chronic diseases like Tuberculosis (TB), heart-attack, cancer newlineetc. The proposed telemedicine setup will facilitate rural patients to access expert newlinedoctor for obtaining diagnosing and treatment. When patients are in emergency condition newlinein ambulatory service, the patient s vital sign and diagnosis details which needs newlineto reach nearby hospital doctor for quick start of treatment process, thereby saves diagnosis newlinetime and life of the patient. Hence, the proposed telemedicine setup will help in newlinediagnosis and facilitate quick treatment of emergency patients. Patients undergoing post newlinetreatment of Tuberculosis (TB), cancer and heart-attack are unable to travel very often newlinefor long distance to consult doctor for reviewing their treatment progress in phased manner. newlineSuch scenarios, the proposed telemedicine setup will help the patients for getting newlineexpert doctor consultation and post review treatments. In all cases, telemedicine routing newlinepath must have less average delay, low packet loss rate and high throughput for sending newlinepatients vital signs and diagnosis details when they are under critical emergency and newlinedelay-sensitive conditions. newlineTelemedicine helps to find the sudden unexpected onset of a health condition requiring newlineimmediate medical treatment. Telemedicine-Hidden Markov Model and Telemedicine- newlineBayesian Network Model was proposed for classifying emergency patients, delay-tolerant newlinepatients and general patients and also their diseases. In addition, an optimal energydistance newline