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Title: Classification and Prediction of Low-Density Lipoprotein Cholesterol LDL-C in The Palestinian Patients Using Machine Learning Techniques
Authors: Malaysha, Sanad $AAUP$Palestinian
Awad, Mohammed $AAUP$Palestinian
Hadrob, Rami $AAUP$Palestinian
Keywords: Cholesterol
Neuro-fuzzy, ANNs, SVM, Regression, RNNs, RBFNNs, Fuzzy logic.
Issue Date: 1-Feb-2022
Publisher: Intelligent Networks and Systems Society (INASS )
Citation: International Journal of Intelligent Engineering and Systems
Series/Report no.: VOL 15, NO. 1;PP: 453-463
Abstract: Cholesterol is one of the major causes of health problems in Palestine and globally in the world, with percentages of 31.5% and 31.4%, respectively. Cholesterol has four main values which are the Total Cholesterol TCH, Triglycerides TG, Low-Density Lipoprotein Cholesterol LDL-C, and High-Density Lipoprotein Cholesterol HDL-C. The main level that is a major factor for Cardiovascular Disease CVD is LDL-C that is called bad cholesterol, it builds upon the arteries walls narrowing them and slowing the blood flow feeding the heart and brain, causing heart attacks and strokes. Machine learning (ML) techniques support recognizing and diagnosing the LDL-C, which is based on the past medical history and heuristic data. This research utilized ML techniques for classifying and predicting the LDL-C. Additionally, the techniques applied to the HDL-C classification and prediction. The utilized techniques are the Artificial Neural Networks ANNs, Recurrent Neural Networks RNN, Radial Basis Function Neural Networks RBFNN, Fuzzy Logic, Support Vector Machines SVM, Decision Tress DT, Logistic Regression LR, and a hybrid model of combining the ANNs with Fuzzy Logic for optimizing the results accuracy and reducing the classifying error. The dataset was collected from Palestine with cooperation with the Palestinian Ministry of Health MoH. Another additional supportive international dataset is utilized, which is collected by the Korean National Health and Nutritional Examination Survey KNHANES, it's used to generalize the results and compare with the other efforts. The obtained result outperformed the other efforts done on the same idea with a significant difference, it reached the accuracy of 97.10% in the international dataset and 95.55% in the national dataset
ISSN: . ISSN:2185-310X ,, E-ISSN:2185-3118
Appears in Collections:Faculty & Staff Scientific Research publications

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