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 Computer-Aided Provisional Diagnosis System Using Machine Learning
Tác giả hoặc Nhóm tác giả: Vu-Thu-Nguyet Pham, Quang-Chung Nguyen, Van-To-Thanh Nguyen, and Quang-Vu Nguyen
Nơi đăng: The 1st International Conference on Intelligence of Things - ICIT 2022; Số: Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 148);Từ->đến trang: 167–174;Năm: 2022
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
The volume of fresh information from scientific researches is expanding at a quicker rate due to the rapid growth of technology. Because there is so much data, doctors have a difficult time diagnosing the disease, which can lead to confusion. Every three years, the volume of medical information doubles. It is estimated that a doctor needs to read 29 h every day to remain up to date on all medical material. Furthermore, big data sources such as data from electronic health records (EHRs), “omic” data – genomics data, metabolic data, proteomics data, as well as sociodemographic and lifestyle data, are data sources that would be useless without extensive analysis. Artificial Intelligence (AI) technology is the only way to obtain access to and utilise huge amounts of information in the medical profession. Besides that, an accurate and timely examination of any health-related problem is critical for sickness prevention and treatment. This study proposed an AI-based system that can generally predict diseases based on patients’ symptoms. We have designed our system using many Machine Learning (ML) algorithms such as Naïve Bayes, Random Forests, and Decision Trees.
ABSTRACT
The volume of fresh information from scientific researches is expanding at a quicker rate due to the rapid growth of technology. Because there is so much data, doctors have a difficult time diagnosing the disease, which can lead to confusion. Every three years, the volume of medical information doubles. It is estimated that a doctor needs to read 29 h every day to remain up to date on all medical material. Furthermore, big data sources such as data from electronic health records (EHRs), “omic” data – genomics data, metabolic data, proteomics data, as well as sociodemographic and lifestyle data, are data sources that would be useless without extensive analysis. Artificial Intelligence (AI) technology is the only way to obtain access to and utilise huge amounts of information in the medical profession. Besides that, an accurate and timely examination of any health-related problem is critical for sickness prevention and treatment. This study proposed an AI-based system that can generally predict diseases based on patients’ symptoms. We have designed our system using many Machine Learning (ML) algorithms such as Naïve Bayes, Random Forests, and Decision Trees.
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