التنبؤ بالامرض بإستخدام آلة المتجهات الداعمة: دراسة حالة علي مرض كوفيد -19

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

روابط وملفات

التعريف والنوع

رقم الوثيقة

052662f4-9ea8-467f-baa3-2ec1c4adc121

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2202306_1.pdf

بيانات النشر

ترجمة العنوان

Disease prediction using support vector machines: Covid-19 case study

ألقاب المؤلفين

[{"name_ar":" سعد صبير","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"إسراء محمد احمد محمد خوجلي","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

Arabic

المصدر والدورية

اسم المصدر

التنبؤ بالامرض بإستخدام آلة المتجهات الداعمة: دراسة حالة علي مرض كوفيد -19

المحتوى والصفحات

عدد الصفحات

87

ترجمة الملخص

The number of people infected with the Corona virus pandemic in various regions of the world has exceeded four million and 636 thousand, and the disease has eliminated approximately 312,000, while about one million and 700 thousand infected have recovered. Signs and symptoms of the Corona virus disease may appear after two to 14 days of exposure. Although symptoms for most people with COVID-19 range from mild to moderate, the disease can cause severe medical complications and be fatal for some people. Older people or those who already have health problems are more likely to become seriously ill when infected with COVID-19. A disease prediction model has been developed that combines several features to estimate the risk of infection. The goal is to assist the medical staff in triaging patients, especially in the context of limited healthcare resources (ie, screening work is not available). We created a machine-learning approach trained on records from 599 individuals, that predicts COVID-19 test results with high accuracy using only eight binary traits: gender, age >60, is there known contact with an infected individual, and five primary clinical symptoms are fever. Shortness of breath, headache, sore throat, and cough. In this study, the SVM model was used, and an accuracy of 82.30%, 76.82% precision and 95.61% recall were obtained.

كلمات الباحثين

الحاسوب هندسة البرمجيات

إشراف وإعداد

الإشراف

سعد صبير

الإعداد

إسراء محمد احمد محمد خوجلي

الاقتباسات الببليوغرافية