الفهرس 
Introduction and ObjectiveOnly 14 pages are availabe for public view
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Abstract
Modern medicine produces a large amount of data which is kept in medical databases.
The extraction of relevant information and provision of scientific decision making for
illness diagnosis and treatment from the database is becoming more essential. Medical
data mining methods and applications based on computation intelligence, such as
artificial neural networks, rough sets, fuzzy systems, and convolution neural networks,
help in designing or developing automated intelligent systems which help health
professionals in diagnosing health problems.
The principal objective of this dissertation is to develop data mining techniques and
artificial intelligence on medical data. Toward this objective, this dissertation develops
three intelligent automated systems that may assist personnel related to healthcare service
to diagnose new patients accurately with improved diagnostic speed., First One: Aim to
early prediction system of breast cancer based on medical images (histopathology images)
to diagnosis breast tumor, using mixing pre-trained deep Convolutional Neural Networks
(CNN) as feature extractor, and multilevel hand-crafted features and feature selection
method (PCA). Experimental results show the accuracy of the proposed method of
96.97%. The Second: Hybrid Prediction System (RS_QA) based on a rough set and quasioptimal (AQ) rule induction algorithm to predict breast cancer. The suggested experiment
is performed using the Coimbra Breast Cancer Dataset (BCCD) based on sets of measures
that can be obtained in routine blood tests. Using classification precision, sensitivity,
specificity, and receiver operating characteristics (ROC) curves, the efficiency of our
suggested approach was assessed. Experimental results indicate the highest classification
accuracy (91.7 percent), sensitivity (83.3 percent), and precision (94.3) obtained by the
proposed (RS_QA) model. The Third system: an expert system for calculating the Risk
Factor for mortality one year after thoracic lung cancer surgery. an interesting hybrid
model combining near sets with soft sets, namely soft near sets. as a system for not only
predicting patient lung survival or not but also, to determine the degree of risk. The correct
survival classification is done with 90.0 % accuracy. Identifying the possibility of lung
cancer surgery will help the doctor and patients make a more informed decision about
locating the treatment methods.