Machine learning approach to predict body weight in adults
Bibliographic Data
| ID | 22080826 |
|---|---|
| Authors | Kazuya Fujihara (0000-0001-6725-4169), Ilídia da Ascenção Garrido Martins Juras (corresponding author), Mayuko Yamada Harada, Chika Horikawa (0000-0003-4213-0252), Midori Iwanaga, Hirofumi Tanaka (0000-0002-1780-7471), Hitoshi Nomura, Yasuharu Sui, Kyouhei Tanabe, Takaho Yamada, Satoru Kodama (0000-0002-9521-7274), Kiminori Kato (0000-0001-6935-8615), Hirohito Sone (0000-0003-1263-2817) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1090146-1090146 |
| Publication date | 2023-06-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1090146 |
| OpenAlex | W37397751 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
The HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management
Physics · Coastal and Marine Management · Fish biology, ecology, and behavior · Marine and fisheries research
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |