機械学習による肝炎患者の生存結果に影響を与える予測要因
1School of Physical Education, Hunan University of Arts and Science, Changde, Hunan, China.
Computer methods in biomechanics and biomedical engineering
|August 31, 2025
まとめ
この研究は機械学習による 肝炎の予測を向上させています DTROモデルは,肝炎を生き延びる可能性のある患者を特定する上で優れた精度を示した.
科学分野:
- ヘパトロジー
- 医療情報学
- 機械学習
背景:
- 肝炎はA-Eウイルスが原因で 肝硬変や癌などの重篤な肝疾患を引き起こす可能性があります
- B型肝炎とC型肝炎は肝不全のリスクを大きく高めます
- 機械学習は患者のデータを分析することで 肝炎のリスクを予測する有望なアプローチを提供します
研究 の 目的:
- 肝炎の生存率を予測するための高度な機械学習モデルを開発し評価する.
- 分類アルゴリズムと最適化技術を組み合わせたハイブリッドモデルの有効性を比較する.
主な方法:
- 決定樹分類 (DTC) と極端な梯度増強分類 (XGBC) を利用した.
- 統合された3つの新しい最適化器:リゾトミー最適化アルゴリズム (ROA),ゴールドラッシュ最適化器 (GRO),モーションエンコードされた電気電荷粒子最適化アルゴリズム (MEPO).
- ハイブリッドモデルの開発:DTRO,XGRO,DTMEにより高い予測精度.
主要な成果:
- DTROハイブリッドモデルは,0.991の最高精度を達成し,ベースラインのDTCを上回りました.
- XGROハイブリッドモデルも0.991に達し,高い精度を示しました.
- DTMEハイブリッドモデルは0.954の精度を達成した.
結論:
- DTROモデルは,肝炎の生存率を予測するのに最も信頼性が高いことが判明しました.
- ハイブリッドの機械学習モデルは,肝炎の予測を改善する大きな可能性を秘めています.
- 最適化された分類アルゴリズムは 肝臓疾患のリスクを管理する上で 精度を高めています
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