Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalities: clinical data, medical history, and medical imaging. The approximate number of instances of each type of feature is recorded on the Count column: it is an indicator of the scale and variety of the dataset. Datas were separated into training (75 percent), validation (10 percent) and test (15 percent) sets to maintain patient level separation to prevent data leakage.

<p>Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalities: clinical data, medical history, and medical imaging. The approximate number of instances of each type of f...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Medhat A. Tawfeek (22522087) (author)
مؤلفون آخرون:	Ibrahim Alrashdi (22522090) (author), Madallah Alruwaili (22522093) (author), Hisham Allahem (22522096) (author)
منشور في:	2025
الموضوعات:	Medicine Sociology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified utilizes advanced algorithms shapley additive explanations particle swarm optimization improve clinical decision iii clinical database support vector machine proposed framework employs proposed framework demonstrate construct patient models based interpretation module hybrid computational framework combined predictive power enable early diagnosis hybrid machine learning cardiovascular disease detection ai framework provides machine learning cardiovascular disease early detection personalized diagnosis patient data guidance based ai framework early intervention hybrid ml therapeutic tools synergistically integrates study seeks shorten delays sensitivity 96 roc 97 risk individuals previous methods preliminary results personalizing treatments might result medical images mathematical model ineffective therapies individual peculiarities improved way identifying high identify high hyperparameter tuning genomic data diagnostic complexity beneficial results ai techniques accurate diagnostic 9 %,
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الوصف
الملخص:	<p>Description, feature counts, and data partitioning of the MIMIC-III clinical database (v1.4) used in this study. The table categorizes patient data into three main relevant modalities: clinical data, medical history, and medical imaging. The approximate number of instances of each type of feature is recorded on the Count column: it is an indicator of the scale and variety of the dataset. Datas were separated into training (75 percent), validation (10 percent) and test (15 percent) sets to maintain patient level separation to prevent data leakage.</p>

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