𝙄𝙣 𝙤𝙪𝙧 𝙤𝙣𝙜𝙤𝙞𝙣𝙜 𝙧𝙚𝙫𝙞𝙚𝙬 𝙤𝙛 𝙈𝙇 𝙖𝙣𝙙 𝙝𝙚𝙖𝙡𝙩𝙝𝙘𝙖𝙧𝙚 𝙥𝙪𝙗𝙡𝙞𝙘𝙖𝙩𝙞𝙤𝙣𝙨, 𝙖 𝙧𝙚𝙘𝙚𝙣𝙩 𝙥𝙖𝙥𝙚𝙧 𝙖𝙜𝙖𝙞𝙣 𝙨𝙝𝙤𝙬𝙨 𝙧𝙚𝙘𝙪𝙧𝙧𝙞𝙣𝙜 𝙘𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚𝙨 𝙞𝙣 𝙘𝙡𝙞𝙣𝙞𝙘𝙖𝙡 𝙈𝙇 𝙪𝙨𝙚:

🔗 doi.org/10.1016/j.ejvs.2025.06.002

The study investigates AAA rupture prediction using several ML models — an important and promising direction. However, 𝙩𝙬𝙤 𝙢𝙚𝙩𝙝𝙤𝙙𝙤𝙡𝙤𝙜𝙞𝙘𝙖𝙡 𝙞𝙨𝙨𝙪𝙚𝙨 appear once again:

1️⃣ 𝙃𝙞𝙜𝙝 𝙛𝙚𝙖𝙩𝙪𝙧𝙚-𝙩𝙤-𝙨𝙖𝙢𝙥𝙡𝙚 𝙧𝙖𝙩𝙞𝙤
The model was trained with more than ten predictors while only 25–100 samples per class were available. Such imbalance makes robust generalization highly unlikely.

2️⃣ 𝘼𝙗𝙨𝙚𝙣𝙘𝙚 𝙤𝙛 𝙖𝙣 𝙞𝙣𝙙𝙚𝙥𝙚𝙣𝙙𝙚𝙣𝙩 𝙩𝙚𝙨𝙩 𝙨𝙚𝙩
Reported performance metrics originate exclusively from internal cross-validation. Without a separate test split, these results represent exposed validation rather than true out-of-sample performance.

𝙄𝙛 𝙮𝙤𝙪 𝙖𝙧𝙚 𝙪𝙣𝙘𝙚𝙧𝙩𝙖𝙞𝙣 𝙖𝙗𝙤𝙪𝙩 𝙮𝙤𝙪𝙧 🅼🅻 𝙘𝙖𝙨𝙚 — 𝙘𝙤𝙣𝙩𝙖𝙘𝙩 𝙪𝙨. 𝙒𝙚 𝙘𝙖𝙣 𝙥𝙧𝙤𝙫𝙞𝙙𝙚 𝙖 𝙛𝙪𝙡𝙡 𝙚𝙣𝙙-𝙩𝙤-𝙚𝙣𝙙 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣.

🔗 mlhs.ink/Service/

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