استخدام بيانات الخزعة المسحوبة لتصنيف أنواع سرطان الثدي عن طريق التعلم الآلي
DOI:
https://doi.org/10.33977/2106-000-008-003الكلمات المفتاحية:
التعلم الآلي (ML)، ، تصنيفات سرطان الثدي، مصنف شجرة القرار (DTC)، ، آلة الدعم المتجه (SVM)، ، مصنف الغابة العشوائية (RFC)، ، سحب الخزعةالملخص
الاهداف: يعد سرطان الثدي السبب الرئيسي للوفاة في جميع أنحاء العالم والأهم في فلسطين، يستفيد غالبا من التشخيص المبكر لتحسين نتائج المرضى. ومع ذلك، فإن تشخيص الأورام الصغيرة بدقة يمكن أن يكون صعبًا، مع ارتفاع مخاطر الخطأ البشري. تهدف هذه الدراسة إلى تعزيز تصنيف سرطان الثدي من خلال الاستفادة من خوارزميات التعلم الآلي.المنهجية: قام البحث بتحليل ومقارنة ثلاث تقنيات للتعلم الآلي - مصنف شجرة القرار (DTC) وآلة المتجهات الداعمة (SVM) ومصنف الغابة العشوائية (RFC) - لتحديد الطريقة الأكثر كفاءة لتصنيف أورام سرطان الثدي. تم تقييم دقة الخوارزميات باستخدام مصفوفة الارتباك على مجموعة بيانات تحتوي على 569 عينة و29 ميزة.النتائج: أظهرت النتائج أن مصنف شجرة القرار (DTC) كان الأكثر نجاحًا، حيث حقق درجات خالية من العيوب بنسبة 100٪ في الدقة والإحكام والحساسية والخصوصية.الخلاصة: وفي الختام، يؤكد البحث على الأداء الممتاز لمصنف شجرة القرار في تصنيف سرطان الثدي، مما قد يحسن بشكل كبير من دقة التشخيص ونتائج المرضى. تشير النتائج إلى أن التشخيص المباشر للمصابين بالسرطان لديه القدرة على أن يكون أداة مفيدة في تقليل الأخطاء التشخيصية وتعزيز التعرف المبكر والرعاية في البيئات الطبية، مما يدفع إلى إجراء دراسات إضافية لتعزيز وتأكيد فعاليته.
المراجع
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