Respiratory diseases are among the most common causes of morbidity and mortality worldwide. Computer-assisted learning tools have been shown to promote self-directed learning and enhance problem-solving and predicting skills. However, few have been developed in the area of respiratory medicine. Thus, we aimed in this study to build a framework to predict the respiratory system diseases by applying Data Mining Techniques (DMT) on clinical information. The framework composed of three main phases which are preprocessing phase, data mining phase and evaluation phase to evaluate the built prediction model. This Approach statistics shows acceptable results.
Saleh Abdu, Ahmed, M. Mostafa, Mostafa-Sami, & Sedky Adly, Aya. (2020). Building a respiratory diseases recognition system Using Data mining techniques. النشرة المعلوماتية في الحاسبات والمعلومات, 2(1), 10-16. doi: 10.21608/fcihib.2020.107526
MLA
Ahmed Saleh Abdu; Mostafa-Sami M. Mostafa; Aya Sedky Adly. "Building a respiratory diseases recognition system Using Data mining techniques", النشرة المعلوماتية في الحاسبات والمعلومات, 2, 1, 2020, 10-16. doi: 10.21608/fcihib.2020.107526
HARVARD
Saleh Abdu, Ahmed, M. Mostafa, Mostafa-Sami, Sedky Adly, Aya. (2020). 'Building a respiratory diseases recognition system Using Data mining techniques', النشرة المعلوماتية في الحاسبات والمعلومات, 2(1), pp. 10-16. doi: 10.21608/fcihib.2020.107526
VANCOUVER
Saleh Abdu, Ahmed, M. Mostafa, Mostafa-Sami, Sedky Adly, Aya. Building a respiratory diseases recognition system Using Data mining techniques. النشرة المعلوماتية في الحاسبات والمعلومات, 2020; 2(1): 10-16. doi: 10.21608/fcihib.2020.107526
)
مشاهدة على SCiNiTO