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Megahed, M., A. Asad, and A. Mohammed, "Data on learners emotional states, mental responses and fuzzy learning flows during interaction with learning environment", ElSEVIER journal Data in Brief, vol. 25, issue 2352-3409, pp. 104378, 2019.
Saad, Y., A. Awad, W. Al Akel, W. Doss, T. Awad, and M. Mabrouk, "Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients.", European journal of gastroenterology & hepatology, vol. 30, issue 2, pp. 201-206, 2018 Feb. Abstract

OBJECTIVES: Hepatitis C virus (HCV) and diabetes mellitus (DM) are prevalent diseases worldwide, associated with significant morbidity, mortality, and mutual association. The aims of this study were as follows: (i) find the prevalence of DM among 71 806 Egyptian patients with chronic HCV infection and its effect on liver disease progression and (ii) using data mining of routine tests to predict hepatic fibrosis in diabetic patients with HCV infection.

PATIENTS AND METHODS: A retrospective multicentered study included laboratory and histopathological data of 71 806 patients with HCV infection collected by Egyptian National Committee for control of viral hepatitis. Using data mining analysis, we constructed decision tree algorithm to assess predictors of fibrosis progression in diabetic patients with HCV.

RESULTS: Overall, 12 018 (16.8%) patients were diagnosed as having diabetes [6428: fasting blood glucose ≥126 mg/dl (9%) and 5590: fasting blood glucose ≥110-126 mg/dl (7.8%)]. DM was significantly associated with advanced age, high BMI and α-fetoprotein (AFP), and low platelets and serum albumin (P≤0.001). Advanced liver fibrosis (F3-F4) was significantly correlated with DM (P≤0.001) irrespective of age. Of 16 attributes, decision tree model for fibrosis showed AFP was most decisive with cutoff of 5.25 ng/ml as starting point of fibrosis. AFP level greater than cutoff in patients was the first important splitting attribute; age and platelet count were second important splitting attributes.

CONCLUSION: (i) Chronic HCV is significantly associated with DM (16.8%). (ii) Advanced age, high BMI and AFP, low platelets count and albumin show significant association with DM in HCV. (iii) AFP cutoff of 5.25 is a starting point of fibrosis development and integrated into mathematical model to predict development of liver fibrosis in diabetics with HCV (G4) infection.

Saad, Y., A. Awad, W. Al Akel, W. Doss, T. Awad, and M. Mabrouk, "Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients.", European journal of gastroenterology & hepatology, vol. 30, issue 2, pp. 201-206, 2018. Abstract

OBJECTIVES: Hepatitis C virus (HCV) and diabetes mellitus (DM) are prevalent diseases worldwide, associated with significant morbidity, mortality, and mutual association. The aims of this study were as follows: (i) find the prevalence of DM among 71 806 Egyptian patients with chronic HCV infection and its effect on liver disease progression and (ii) using data mining of routine tests to predict hepatic fibrosis in diabetic patients with HCV infection.

PATIENTS AND METHODS: A retrospective multicentered study included laboratory and histopathological data of 71 806 patients with HCV infection collected by Egyptian National Committee for control of viral hepatitis. Using data mining analysis, we constructed decision tree algorithm to assess predictors of fibrosis progression in diabetic patients with HCV.

RESULTS: Overall, 12 018 (16.8%) patients were diagnosed as having diabetes [6428: fasting blood glucose ≥126 mg/dl (9%) and 5590: fasting blood glucose ≥110-126 mg/dl (7.8%)]. DM was significantly associated with advanced age, high BMI and α-fetoprotein (AFP), and low platelets and serum albumin (P≤0.001). Advanced liver fibrosis (F3-F4) was significantly correlated with DM (P≤0.001) irrespective of age. Of 16 attributes, decision tree model for fibrosis showed AFP was most decisive with cutoff of 5.25 ng/ml as starting point of fibrosis. AFP level greater than cutoff in patients was the first important splitting attribute; age and platelet count were second important splitting attributes.

CONCLUSION: (i) Chronic HCV is significantly associated with DM (16.8%). (ii) Advanced age, high BMI and AFP, low platelets count and albumin show significant association with DM in HCV. (iii) AFP cutoff of 5.25 is a starting point of fibrosis development and integrated into mathematical model to predict development of liver fibrosis in diabetics with HCV (G4) infection.

Salama, M., Data Mining for Medical Informatics, , Cairo, Cairo Unv, 2012. AbstractThesis.pdfPresentation.pdf

The work presented in this thesis investigates the nature of real-life data, mainly in the medical field, and the problems in handling such nature by the conventional data mining techniques. Accordingly, a set of alternative techniques are proposed in this thesis to handle the medical data in the three stages of data mining process. In the first stage which is preprocessing, a proposed technique named as interval-based feature evaluation technique that depends on a hypothesis that the decrease of the overlapped interval of values for every class label leads to increase the importance of such attribute. Such technique handles the difficulty of dealing with continuous data attributes without the need of applying discretization of the input and it is proved by comparing the results of the proposed technique to other attribute evaluation and selection techniques. Also in the preprocessing stage, the negative effect of normalization algorithm before applying the conventional PCA has been investigated and how the avoidance of such algorithm enhances the resulted classification accuracy. Finally in the preprocessing stage, an experimental analysis introduces the ability of rough set methodology to successfully classify data without the need of applying feature reduction technique. It shows that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine, Hidden Naive Bayesian network, Bayesian network and other techniques.
In the machine learning stage, frequent pattern-based classification technique is proposed; it depends on the detection of variation of attributes among objects of the same class. The preprocessing of the data like standardization, normalization, discretization or feature reduction is not required in this technique which enhances the performance in time and keeps the original data without being distorted. Another contribution has been proposed in the machine learning stage including the support vector machine and fuzzy c-mean clustering techniques; this contribution is about the enhancement of the Euclidean space calculations through applying the fuzzy logic in such calculations. This enhancement has used chimerge feature evaluation techniques in applying fuzzification on the level of features. A comparison is applied on these enhanced techniques to the other classical data mining techniques and the results shows that classical models suffers from low classification accuracy due to the dependence of un-existed presumption.
Finally, in the visualization stage, a proposed technique is presented to visualize the continuous data using Formal Concept Analysis that is better than the complications resulted from the scaling algorithms.

Casagrande, E., W. Woon, and H. Zeineldin, "A Data Mining Approach to Fault Detection for Isolated Inverter-based microgrids", accepted for publication in IET Generation, Transmission & Distribution, 2013.
Shousha, H. I., A. H. Awad, D. A. H. Omran, M. M. Elnegouly, and M. Mabrouk, "Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.", Japanese journal of infectious diseases, vol. 71, issue 1, pp. 51-57, 2018 Jan 23. Abstract

IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health records to discover hidden patterns and relationships. The current study aims to evaluate and compare the prediction accuracy of scoring system like aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index versus data mining for the prediction of HCV-related advanced fibrosis. This retrospective study included 427 patients with chronic hepatitis C. We used data mining analysis to construct a decision tree by reduced error (REP) technique, followed by Auto-WEKA tool to select the best classifier out of 39 algorithms to predict advanced fibrosis. APRI and FIB-4 had sensitivity-specificity parameters of 0.523-0.831 and 0.415-0.917, respectively. REPTree algorithm was able to predict advanced fibrosis with sensitivity of 0.749, specificity of 0.729, and receiver operating characteristic (ROC) area of 0.796. Out of the 16 attributes, IL28B genotype was selected by the REPTree as the best predictor for advanced fibrosis. Using Auto-WEKA, the multilayer perceptron (MLP) neural model was selected as the best predictive algorithm with sensitivity of 0.825, specificity of 0.811, and ROC area of 0.880. Thus, MLP is better than APRI, FIB-4, and REPTree for predicting advanced fibrosis for patients with chronic hepatitis C.

Shousha, H. I., A. H. Awad, D. A. H. Omran, M. M. Elnegouly, and M. Mabrouk, "Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.", Japanese journal of infectious diseases, vol. 71, issue 1, pp. 51-57, 2018. Abstract

IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health records to discover hidden patterns and relationships. The current study aims to evaluate and compare the prediction accuracy of scoring system like aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index versus data mining for the prediction of HCV-related advanced fibrosis. This retrospective study included 427 patients with chronic hepatitis C. We used data mining analysis to construct a decision tree by reduced error (REP) technique, followed by Auto-WEKA tool to select the best classifier out of 39 algorithms to predict advanced fibrosis. APRI and FIB-4 had sensitivity-specificity parameters of 0.523-0.831 and 0.415-0.917, respectively. REPTree algorithm was able to predict advanced fibrosis with sensitivity of 0.749, specificity of 0.729, and receiver operating characteristic (ROC) area of 0.796. Out of the 16 attributes, IL28B genotype was selected by the REPTree as the best predictor for advanced fibrosis. Using Auto-WEKA, the multilayer perceptron (MLP) neural model was selected as the best predictive algorithm with sensitivity of 0.825, specificity of 0.811, and ROC area of 0.880. Thus, MLP is better than APRI, FIB-4, and REPTree for predicting advanced fibrosis for patients with chronic hepatitis C.

Shousha, H. I., A. H. Awad, D. A. H. Omran, M. M. Elnegouly, and M. Mabrouk, "Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.", Japanese journal of infectious diseases, vol. 71, issue 1, pp. 51-57, 2018. Abstract

IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health records to discover hidden patterns and relationships. The current study aims to evaluate and compare the prediction accuracy of scoring system like aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index versus data mining for the prediction of HCV-related advanced fibrosis. This retrospective study included 427 patients with chronic hepatitis C. We used data mining analysis to construct a decision tree by reduced error (REP) technique, followed by Auto-WEKA tool to select the best classifier out of 39 algorithms to predict advanced fibrosis. APRI and FIB-4 had sensitivity-specificity parameters of 0.523-0.831 and 0.415-0.917, respectively. REPTree algorithm was able to predict advanced fibrosis with sensitivity of 0.749, specificity of 0.729, and receiver operating characteristic (ROC) area of 0.796. Out of the 16 attributes, IL28B genotype was selected by the REPTree as the best predictor for advanced fibrosis. Using Auto-WEKA, the multilayer perceptron (MLP) neural model was selected as the best predictive algorithm with sensitivity of 0.825, specificity of 0.811, and ROC area of 0.880. Thus, MLP is better than APRI, FIB-4, and REPTree for predicting advanced fibrosis for patients with chronic hepatitis C.

A.Badr, S.El-Gamal, and N.Hamza, "A Data Integration XML Tool", INFOS, 2004.
Abdelgawad, H., T. Abdulazim, B. Abdulhai, A. Hadayeghi, and W. Harrett, "Data imputation and nested seasonality time series modelling for permanent data collection stations: methodology and application to Ontario", Canadian Journal of Civil Engineering, vol. 42, no. 5: NRC Research Press, pp. 287–302, 2015. Abstract
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Ishida, T., J. Fang, E. Fathalla, and T. Furukawa, "Data driven maintenance cycle focusing on deterioration mechanism of road bridge RC decks", Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations: CRC Press, pp. 1204-1210, 2021. Abstract
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Ishida, T., J. Fang, E. Fathalla, and T. Furukawa, "Data driven maintenance cycle focusing on deterioration mechanism of road bridge RC decks", Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations: CRC Press, pp. 1204-1210, 2021. Abstract
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Shahin, H., M. F. Shaaban, M. H. Ismail, H. - A. M. Mourad, and A. Khattab, "Data Collection in Advanced Metering Infrastructure Using UAVs", Proc. of the 4th Int. Conf. on Communications, Signal Processing, and their Applications (ICCSPA’20), March 16-18, 2021.
Shahin, H., M. F. Shaaban, M. H. Ismail, H. - A. Mourad, and A. Khattab, "Data Collection in Advanced Metering Infrastructure Using UAVs", IEEE International Conference on Communications, Signal Processing and their Applications (ICCSPA), Sharjah, UAE, IEEE, 2021.
Salah Ali, D., A. Ghoneim, and S. M., "Data Clustering Method based on Mixed Similarity Measures", The 6th International Conference on Operations Research and Enterprise Systems , Porto, Portugal., February 23-25, 2017.
Ali, D. S., A. Ghoneim, and M. Saleh, "Data Clustering Method based on Mixed Similarity Measures", ICORES, 2017.
Bastawissy, A. H. E., O. Hegazy, and A.Z.ElQutaany, Data cleaning in the virtual data integration environment, , Cairo, Cairo University, 2011.
Attia, A., N. E. Khalifa, and A. M. I. R. A. KOTB, "Data Backup Approach using Software-defined Wide Area Network", International Journal of Advanced Computer Science and Applications, vol. 12, issue 12, pp. 309-316, 2021.
Abu El Ela, M., "Data Analysis Methodology for Reservoir Management", SPE-106899, The SPE EUROPEC/EAGE Annual Conference and Exhibition, London, UK, 2007.
Ata, M., M. Abouhamad, M. H. Serror, and M. Marzouk, "Data Acquisition and Structural Analysis for Bridge Deck Condition Assessment using Ground Penetration Radar", Journal of Performance of Constructed Facilities, vol. 35, issue 5, pp. 04021064, 2021.
Ata, M., M. Abouhamad, M. H. Serror, and M. Marzouk, "Data Acquisition and Structural Analysis for Bridge Deck Condition Assessment Using Ground Penetration Radar", Journal of Performance of Constructed Facilities, vol. 35, issue 5, 2021.
Ata, M., M. Abouhamad, M. H. Serror, and M. Marzouk, "Data Acquisition and Structural Analysis for Bridge Deck Condition Assessment Using Ground Penetration Radar", Journal of Performance of Constructed Facilities, vol. 35, issue 5, 2021.
Abdelhai, R., G. Khafagy, and H. Helmy, "DASH Diet: How Much Time Does It Take to Reduce Blood Pressure in Pre-hypertensive and Hypertensive Group 1 Egyptian patients?", Middle-East Journal of Family Medicine, vol. 13, issue 5, pp. 12-20, 2015.
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