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Performance Evaluation of Statistical and Machine Learning Models

المصدر: مجلة الدراسات والبحوث التجارية
الناشر: جامعة بنها - كلية التجارة
المؤلف الرئيسي: Ramadan, Mervat M. (Author)
مؤلفين آخرين: Eltelbany, Dina S. (Co-Author) , Hegazy, Ayman S. (Co-Author)
المجلد/العدد: س43, ع3
محكمة: نعم
الدولة: مصر
التاريخ الميلادي: 2023
الشهر: سبتمبر
الصفحات: 286 - 298
ISSN: 1110-1547
رقم MD: 1551543
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
كلمات المؤلف المفتاحية:
Machine Learning | ROC Curve | Confusion Matrix
رابط المحتوى:
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LEADER 02665nam a22002417a 4500
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041 |a eng 
044 |b مصر 
100 |9 822693  |a Ramadan, Mervat M.  |e Author 
245 |a Performance Evaluation of Statistical and Machine Learning Models 
260 |b جامعة بنها - كلية التجارة  |c 2023  |g سبتمبر 
300 |a 286 - 298 
336 |a بحوث ومقالات  |b Article 
520 |b Machine learning algorithms have gained popularity in recent years in many fields due to their promising results in predictive performance of classification problems. The application of machine-learning algorithms has also been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages (such as R or Python). Machine learning is a subsection of Artificial Intelligence (AI), it is one of the most promising tools in classification and it a model that aims to discover the unknown function, dependence, or structure between input and output variables. This study proposes statistical and machine learning models to diagnose anemia disease. Some machine learning techniques have been used in this work to avoid over fitting, pre-process the data and adjust the outliers to give better results. Three classifiers, including Logistic Regression, k-Nearest Neighbor and Decision Tree are implemented in this work. The performance of the models is evaluated based confusion matrix, recall, precision, f1-score, accuracy, Matthews correlation coefficient and ROC curve to compute area under the curve (AUC). The results show the logistic regression has the highest accuracy of 99.57%, with recall values of 99.41%, precision values of 99.61, f1-score of 99.51% and Matthews correlation coefficient values of 99.13%. Decision tree has the second highest accuracy of 98.64%, with recall values of 99.02%, precision values of 97.87, f1-score of 98.44%, and Matthews correlation coefficient values of 97.23%. 
653 |a الذكاء الاصطناعي  |a التعلم الآلي  |a البرمجة الإحصائية  |a مصفوفة الارتباك 
692 |b Machine Learning  |b ROC Curve  |b Confusion Matrix 
700 |9 822690  |a Eltelbany, Dina S.  |e Co-Author 
700 |9 822711  |a Hegazy, Ayman S.  |e Co-Author 
773 |4 الإدارة  |4 الاقتصاد  |6 Management  |6 Economics  |c 011  |f Mağallaẗ Al-Dirāsāt wa Al-Buḥūṯ Al-Tiǧāriyyaẗ  |l 003  |m س43, ع3  |o 1918  |s مجلة الدراسات والبحوث التجارية  |t Journal of Studies and Business Research  |v 043  |x 1110-1547 
856 |u 1918-043-003-011.pdf 
930 |d y  |p y  |q n 
995 |a EcoLink 
999 |c 1551543  |d 1551543 

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