Development of Artificial Neural Network Model for Medical Specialty Recommendation

被引：0

作者：

Hasuki, Winda ^{[1
]}

Agustriawan, David ^{[1
]}

Parikesit, Arli Aditya ^{[1
]}

Sadrawi, Muammar ^{[1
]}

Firmansyah, Moch ^{[2
]}

Whisnu, Andreas ^{[3
]}

Natasya, Jacqulin ^{[1
]}

Mathew, Ryan ^{[4
]}

Napitupulu, Florensia Irena ^{[5
]}

Ratnasari, Nanda Rizqia Pradana ^{[1
]}

机构：

[1] Indonesia Int Inst Life Sci, Sch Life Sci, Dept Bioinformat, Jakarta 13210, Indonesia

[2] Lira Medika Hosp, Informat Technol Dept, Jawa Barat 41314, Indonesia

[3] Indonesia Int Inst Life Sci, Informat Technol Dept, Jakarta 13210, Indonesia

[4] Indonesia Int Inst Life Sci, Sch Life Sci, Dept Biotechnol, Jakarta 13210, Indonesia

[5] Indonesia Int Inst Life Sci, Sch Life Sci, Dept Food Sci, Jakarta 13210, Indonesia

来源：

PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY | 2023年 / 31卷 / 06期

关键词：

Machine learning; medical specialty; multilayer perceptron; neural network; recommendation; SMALL DATASET;

D O I：

10.47836/pjst.31.6.05

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Timely diagnosis is crucial for a patient's future care and treatment. However, inadequate medical service or a global pandemic can limit physical contact between patients and healthcare providers. Combining the available healthcare data and artificial intelligence methods might offer solutions that can support both patients and healthcare providers. This study developed one of the artificial intelligence methods, artificial neural network (ANN), the multilayer perceptron (MLP), for medical specialist recommendation systems. The input of the system is symptoms and comorbidities. Meanwhile, the output is the medical specialist. Leave one out cross-validation technique was used. As a result, this study's F1 score of the model was about 0.84. In conclusion, the ANN system can be an alternative to the medical specialist recommendation system.

引用

页码：2723 / 2733

页数：11

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