Résumé:
Detecting disease before it occurs is one of the most important factors in medical treatment. In recent years, the medical field has seen a huge expansion in the field of computer science, such as machine learning and deep learning, these modern techniques have been widely used to detect various diseases. This preventive procedure is essential to treat diseases at an early stage before they develop into more devastating diseases.
The objective of our project is the detection of diseases using supervised machine learning methods. To do this, we used four supervised classification algorithms: Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN) and Logistic regression (LR), to find the one with the highest performance.
The selected algorithms are used for the prediction of two diseases: diabetes and heart disease. The obtained results prove the efficiency of our improved algorithms. Specially, KNN who got the best performance.
يعد اكتشاف المرض قبل حدوثه من أهم العوامل في العلاج الطبي. في السنوات الأخيرة، شهد المجال
الطبي توسعًا هائلاً في مجال علوم الكمبيوتر، مثل التعلم الآلي والتعلم العميق، وقد تم استخدام هذه التقنيات
الحديثة على نطاق واسع للكشف عن الأمراض المختلفة. هذا الإجراء الوقائي ضروري لعلاج الأمراض في
مرحلة مبكرة قبل أن تتطور إلى أمراض أكثر تدميراً.
الهدف من مشروعنا هو الكشف عن الأمراض باستخدام طرق التعلم الآلي الخاضعة للإشراف. للقيام
بذلك، استخدمنا أربع خوارزميات تصنيف خاضعة للإشراف: دعم آلة المتجهات ) SVM ( ، شجرة القرار
( DT ( ، خوارزمية أقرب جيران K (KNN) والانحدار اللوجستي ) LR ( ، للتعرف على أحسن خوارزمي
من حيث الأداء.
استخدمت الخوارزميات المختارة للتنبؤ بمرضين: مرض السكري وأمراض القلب. النتائج التي تم
الحصول عليها تثبت كفاءة خوارزمياتنا المحسنة. على وجه الخصوص، KNN الذي حصل على أفضل
أداء
Detecting disease before it occurs is one of the most important factors in medical treatment. In recent years, the medical field has seen a huge expansion in the field of computer science, such as machine learning and deep learning, these modern techniques have been widely used to detect various diseases. This preventive procedure is essential to treat diseases at an early stage before they develop into more devastating diseases.
The objective of our project is the detection of diseases using supervised machine learning methods. To do this, we used four supervised classification algorithms: Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN) and Logistic regression (LR), to find the one with the highest performance.
The selected algorithms are used for the prediction of two diseases: diabetes and heart disease. The obtained results prove the efficiency of our improved algorithms. Specially, KNN who got the best performance
