HOSPITAL LENGTH OF STAY PREDICTION BASED ON PATIENT EXAMINATION USING NEURAL NETWORK
Abstract
Length of stay (LOS) or length of stay is the main indicator in improving health services, which is expected to continue to increase along with population growth. The population of Indonesia is included in the largest category in the world. This was followed by an increase in the number of inpatient and emergency unit visits which had a high burden of care costs. This study aims to provide a solution by predicting LOS using Neural Network (NN). Predictions can be used as a consideration in improving effective and efficient health services. To improve the performance of the NN algorithm, we implement parameter optimization using the Grid Search to find the combination of the number of epochs, learning rate and momentum that can produce the best accuracy value. The results showed that NN could predict LOS with an accuracy rate of 89.22% when using the default parameter. Meanwhile, by performing parameter optimization using the Grid Search to find the ideal combination of parameters for learning rate, momentum and epoch, the accuracy rate is increased to 92.20%.
Keywords: length of stay, neural network, patient examination, machine learning
Length of stay (LOS) atau lama rawat inap merupakan indikator utama dalam peningkatan pelayanan kesehatan yang diperkirakan akan terus meningkat bersamaan dengan jumlah pertumbuhan penduduk. Jumlah penduduk Indonesia termasuk dalam kategori terbanyak di dunia. Hal ini diikuti dengan pertambahan jumlah kunjungan pasien rawat inap dan unit gawat darurat yang memiliki beban biaya perawatan yang tinggi. Penelitian ini bertujuan untuk memberikan solusi dengan melakukan prediksi LOS menggunakan Neural Network (NN). Prediksi dapat digunakan sebagai pertimbangan dalam peningkatan pelayanan kesehatan yang efektif dan efisien. Untuk meningkatkan performansi dari algoritma NN, kami menerapkan optimasi parameter menggunakan Grid Search untuk menemukan kombinasi jumlah epoch, learning rate dan momentum yang dapat menghasilkan nilai akurasi terbaik. Hasil penelitian menunjukkan bahwa NN dapat memprediksi LOS dengan tingkat akurasi 89,22% jika menggunakan default parameter. Sedangkan dengan melakukan optimasi parameter menggunakan Grid Search untuk menemukan kombinasi ideal parameter learning rate, momentum dan epoch, maka tingkat akurasi meningkat menjadi 92,20%.
Kata kunci: length of stay, neural network, patient examination, machine learning
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DOI: http://dx.doi.org/10.20527/klik.v8i1.368
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