ANALYSIS OF PATIENT ATTENDANCE RATES USING RUSBOOST

Widodo; Dyah Ika Krisnawati; Saifullah Azhar; Fatika La Viola Ifanka; Muhammad Ilham Aziz; Satria Pradana Rizky Yulianto; Devi Ratnasari; Muhammad Oktoda Noorrohman

doi:10.54443/ijset.v4i11.1110

ANALYSIS OF PATIENT ATTENDANCE RATES USING RUSBOOST

Widodo

Dyah Ika Krisnawati

Saifullah Azhar

Fatika La Viola Ifanka

Muhammad Ilham Aziz

Satria Pradana Rizky Yulianto

Devi Ratnasari

Muhammad Oktoda Noorrohman

Patients have the option of undergoing examinations and treatment without having to stay in the hospital. The number of clinics serving patients continues to grow due to the high demand and busy schedules faced by patients. However, hospitals and clinics are still operating well because there are patients who need services, both outpatient and inpatient. In many countries, numerous clinics and hospitals have not implemented an effective data management system for outpatient queues. This results in a number of registered patients not showing up for their appointments, which is certainly detrimental to the nurses and doctors on duty that day. This situation is a loss for clinics and hospitals because manual data management prevents them from predicting the number of patients who will visit. One way to organize patient visit data, both for outpatient and inpatient care, is to utilize big data. The method used in processing this data is Decision Tree classification with Rusboost. By applying Decision Tree classification and Rusboost, we can obtain more accurate predictions, thereby assisting in decision-making.

Keywords: decision tree classification machine learning Rusboost

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