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Updated: Jan 8, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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説明可能なLSTM-LIME統合可視化に基づく深層学習フレームワークを用いた肥満予測

Norah S Alsulami1,2, Muhammad Sher Ramzan3, Bander A Alzahrani3

  • 1Department of Information Systems, Faculty of Computer Science and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. nalsulami0391@stu.kau.edu.sa.

Scientific reports
|December 21, 2025
PubMed
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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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この要約は機械生成です。

本研究では、サウジのデータを用いた肥満予測のための説明可能な深層学習モデルを開発した。双方向長短期記憶(Bi-LSTM)モデルは96%の精度を達成し、肥満リスク因子に関する解釈可能な洞察を提供した。

キーワード:
深層学習説明可能なAIインタラクティブインターフェースLIME可視化LSTM肥満度予測

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