التنبؤ بمرض الكلى المزمن باستخدام طرق اختيار الميزات بالتصفية والتغليف مع تقنيات التعلم الآلي
محتوى المقالة الرئيسي
الملخص
مرض الكلى المزمن (CKD) هو حالة تتميز بفقدان تدريجي لوظيفة الكلى على مدى شهور أو سنوات. يُعد التنبؤ بهذا المرض قضية حيوية في المجال الطبي. لذلك، فإن أداة آلية تستخدم تقنيات التعلم الآلي لتقييم حالة الكلى لدى المريض ستكون مفيدة للأطباء في التنبؤ بمرض الكلى المزمن وتحسين العلاج. في عملية التعلم الآلي، تعتبر مرحلة المعالجة المسبقة خطوة أساسية لتحسين جودة البيانات. يُعد اختيار الميزات إحدى طرق المعالجة المسبقة الرئيسية، حيث يزيل الميزات غير ذات الصلة أو الزائدة، وبالتالي يبسط النموذج ويقلل من عدد الميزات. يستكشف هذا البحث إمكانات طرق اختيار الميزات المختلفة. تصنف طرق اختيار الميزات إلى طرق التصفية (f_classif، chi2) وطرق الالتفاف (إزالة الميزات التكرارية مع التحقق المتقاطع (RFECV) باستخدام مصنف الغابات العشوائية وSVC. تم استخدام دقة المصنفات لتقييم أداء مجموعة البيانات الكاملة مقارنة بالمجموعات الفرعية التي تم إنشاؤها باستخدام اختيار الميزات. تظهر النتائج أن طريقة اختيار الميزات RFECV+SVM تتفوق على غيرها، حيث تقدم أفضل أداء من خلال تحسين الدقة في 5 من أصل 6 مصنفات.
تفاصيل المقالة
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المراجع
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