My Kardio, Remote Heart Monitor

Cardiovascular disease is the number 1 cause of death in the world. In 2015 it was reported that approximately 17 million people died from coronary heart disease and 6.7 million others due to stroke. From WHO data in 2014 in Indonesia cardiovascular disease is the number 1 cause of death, which mentioned 37% of deaths due to cardiovascular disease.

Heart disease, known as cardiovascular disease is a condition in which there is narrowing or blockage of blood vessels that can cause heart attacks.

Sufferers of this disease are also caused by the low quality of health services, and the lack of doctors in remote areas.

Dr. I Ketut Agung Enriko found telemedical technological innovations that are useful to help doctors diagnose and treat cardiovascular disease. On May 9, 2018 in his dissertation session which took place in the Chevron Room, Dean Building, FTUI in his dissertation proposed a technology-based system that could be a solution to the problem of cardiobascular disease in Indonesia.

"Design and Implementation of a Machine to Machine (M2M) System for Cardiovascular Disease Patients with the Auto Recommended Feature using the k-Nearest Neighbor (kNN) Algorithm" talks about a machine-to-machine (M2M) based system to check patient health.

With an application that uses a website and a mobile application called My Cardio, the system of this tool will report the results of health checks to a remote cardiologist. And equipped with auto-recommendation predictions to provide recommendations to doctors in determining the diagnosis of the disease suffered by patients.

With the k-Nearest Neighbors (kNN) algorithm, this tool can make auto recommendations that prove to be quite good in terms of accuracy and speed. Trials have been conducted in the suburbs of Jakarta, namely Banjasari Village (10 patients), Cibubur (15 patients), Cimanggis (37 patients), and Pancoran (23 patients) with a total of 85 patients.

With the results of 2 evaluations namely Quantitative Evaluation and Qualitative Evaluation, Quantitative Evaluation produces an average accuracy of auto recommendation system prediction is 76.47%, the auto recommendation system processing time is 1 second and the performance of data transder time from the inspection location to the M2M server is 8, 97 seconds.

Qualitative evaluation was done through interviews of cardiologists, and the results showed that the application of My Cardio was very helpful especially in areas lacking cardiologist, and was beneficial for large cities where patient access to cardiologists was also constrained by doctor's practice time, and congestion in Jakarta.


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