Preferred Language
Articles
/
HBfQsJIBVTCNdQwC5b4Y
Diabetes Prediction Using Machine Learning
...Show More Authors

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five attributes of the training process. The results of the second experiment showed improvement in the performance of the KNN and the Multilayer Perceptron. The results of the second experiment showed a slight decrease in the performance of the Random Forest with 97.5 % accuracy.

Scopus Crossref
View Publication
Publication Date
Tue Dec 26 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Belief about Medications Among Type 2 Diabetic Patients Attending the National Diabetes Center in Iraq.
...Show More Authors

Diabetes mellitus is a common health problem worldwide counting about 1.2 million cases in Iraq in 2015. Taking in account of the patient’s beliefs about the prescribed medication had been reported to be one of the most important factors that affects adherence where holding positive beliefs about medications is a prerequisite for intentional adherence. The aim of the current study was to investigate and assess beliefs about medicines among type 2 diabetic patients and to determine possible association between this belief and glycemic control as well as some patient-specific factors. This study is a cross-sectional study carried out on 380 (mean age 56.58± 10.06 years) already diagnosed T2DM patients who attended the National Diabetes

... Show More
View Publication Preview PDF
Crossref
Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Hepatocellular Carcinoma Prediction and early Diagnosis of Hepatitis B and C viral infection using miR-122 and miR-223 in a sample of Iraqi patients.
...Show More Authors

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death. Therefore, it is critical for researchers to understand molecular biology in greater depth.  In several diseases including cancer, abnormal miRNA expression has been linked to apoptosis, proliferation, differentiation, and metastasis. Many miRNAs have been studied in relation to cancer, including miR-122, miR-223, and others. Hepatitis B and C viruses are the most important global risk factors for HCC. This study is intended to test whether serum miRNAs serve as a potential biomarker for both HCC and viral infections HBV and C. The expression of miRNA in 64 serum samples was analyzed by RT-qPCR. Compared to healthy volunteers, HCC patients' sera expre

... Show More
View Publication Preview PDF
Scopus (7)
Crossref (2)
Scopus Crossref
Publication Date
Sun Sep 06 2015
Journal Name
Baghdad Science Journal
The effect of obesity and Insulin Resistance on Liver Enzymes in Type2 Diabetes Mellitus
...Show More Authors

Diabetes mellitus (DM) has been defined as a clinical syndrome that is characterized by abnormal carbohydrate metabolism. The chronic hyperglycemia of diabetes is associated with long term damage, dysfunction, and failure of different organs, especially the liver .This study was conducted to assess the effect obesity and insulin resistance on liver enzymes in diabetic Iraqi patients.A comparative study of (90) Iraqi adults divided to three subgroup(30) obese ,(30) nonobese diabetic patients and(30)person had used as control. The analysis included Liver enzyme ALP,ALT,AST,GGT ,Fasting Plasma Glucose (FBG) , Lipid Profile , Hemoglobin A1C , insulin and homeostasis model assessment of insulin resistance (HOMA IR) were measured. Subjects

... Show More
View Publication Preview PDF
Crossref
Publication Date
Tue Jun 01 2021
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
...Show More Authors

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

... Show More
View Publication Preview PDF
Scopus (1)
Scopus Crossref
Publication Date
Sat Oct 06 2012
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
...Show More Authors

Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Applied Engineering Science
Rutting prediction of hot mix asphalt mixtures reinforced by ceramic fibers
...Show More Authors

One of the most severe problems with flexible asphalt pavements is permanent deformation in the form of rutting. Accordingly, the practice of adding fiber elements to asphalt mix to improve performance under dynamic loading has grown significantly in order to prevent rutting distress and ensure a safe and long-lasting road surface. This paper explores the effects of a combination of ceramic fiber (CF), a low-cost, easily available mineral fiber, and thermal insulator fiber reinforced to enhance the Marshall properties and increase the rutting resistance of asphalt mixes at high temperatures. Asphalt mixtures with 0%, 0.75%, 1.5%, and 2.25% CF content were prepared, and Marshall stability and wheel tracking tests were employed to stu

... Show More
Scopus (11)
Crossref (8)
Scopus Crossref
Publication Date
Sat Jul 22 2023
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
...Show More Authors

This paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing
...Show More Authors

Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

... Show More
View Publication
Scopus (2)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Sun Oct 01 2023
Journal Name
Journal Of Applied Hematology
D-dimer and Ferritin Levels in Prediction of COVID-19 Severity
...Show More Authors
Abstract<sec> <title>BACKGROUND:

The most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.

AIM OF THE STUDY:

The aim of the study is to demonstrate the role of COVID-19 on coagulation parameters D-dimer and ferritin with their association with COVID-19 severity and disease progression in a single-center study.

... Show More
View Publication
Scopus Crossref
Publication Date
Mon Dec 02 2024
Journal Name
Engineering, Technology &amp; Applied Science Research
An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
...Show More Authors

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

... Show More
View Publication
Scopus (3)
Crossref (2)
Scopus Crossref