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Sequential feature selection for heart disease detection using random forest
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Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential feature selection approach plays significant role in improving the performance of the proposed model. The proposed feature selection approach is evaluated using real world clinical heart disease dataset collected from University of California Irvine (UCI) data repository. Empirical test on validation set reveals that the proposed model performs well as compared to the existing methods. Overall, the state of-the-art heart disease detection model with classification accuracy of 98.53% is proposed for heart disease detection using SFS and random forest model.

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Publication Date
Fri Aug 13 2021
Journal Name
Neural Computing And Applications
Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
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Publication Date
Tue Sep 29 2020
Journal Name
Iraqi Journal Of Science
Ultrasound Images Registration Based on Optimal Feature Descriptor Using Speeded Up Robust Feature
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Image registration plays a significant role in the medical image processing field. This paper proposes a development on the accuracy and performance of the Speeded-Up Robust Surf (SURF) algorithm to create Extended Field of View (EFoV) Ultrasound (US) images through applying different matching measures. These measures include Euclidean distance, cityblock distance, variation, and correlation in the matching stage that was built in the SURF algorithm. The US image registration (fusion) was implemented depending on the control points obtained from the used matching measures. The matched points with higher frequency algorithm were proposed in this work to perform and enhance the EFoV for the US images, since the maximum accurate matching po

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Feature Extraction Using Remote Sensing Images
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Feature extraction provide a quick process for extracting object from remote sensing data (images) saving time to urban planner or GIS user from digitizing hundreds of time by hand. In the present work manual, rule based, and classification methods have been applied. And using an object- based approach to classify imagery. From the result, we obtained that each method is suitable for extraction depending on the properties of the object, for example, manual method is convenient for object, which is clear, and have sufficient area, also choosing scale and merge level have significant effect on the classification process and the accuracy of object extraction. Also from the results the rule-based method is more suitable method for extracting

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Publication Date
Wed May 10 2023
Journal Name
Diagnostics
A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

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Publication Date
Tue May 11 2021
Journal Name
Research J. Pharm. And Tech
Association of Serum Renal Function Levels with Heart Failure Disease in Iraqi Patients
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Renal function tests are commonly used in clinical practice to look for renal disease, the most common includes the serum urea, uric acid and creatinine. Heart failure patients have a higher incidence of renal function test abnormalities than individuals who do not have heart failure disease. Fifty subjects of adults (male) were divided in to two groups, 25 subjects (healthy) as control (group1) and 25 subjects with heart failure (group 2). Our results indicate that serum uric acid, urea, and creatinine values were significantly elevated (P≤0.05) in patients group (2) compared with healthy group (1). The results also showed, the effect of age categories on uric acid blood urea nitrogen and creatinine values (P≤0.05) and there were no si

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Publication Date
Wed Jan 30 2013
Journal Name
Al-kindy College Medical Journal
Coronary Angiographic Findings in Diabetic Patients Versus non-Diabetics with Coronary Heart Disease
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Background :Atherosclerosis is the most
frequent underlying cause of ischemic heart
disease and a major cause of death all over the
world. This study was carried out to analyze and
compare the angiographic findings in patients
with diabetes mellitus versus non diabetics with
coronary heart disease , and to correlate these
findings with some risk factors for coronary
heart disease.
Methods: A total of 100 patients were studied,
50 with diabetes mellitus, and 50 non diabetics.
This study was carried out at Al-Sadr teaching
hospital in Basrah, Southern Iraq during the
period April 2009- September 2009. All patients
were known to have coronary heart disease. Risk
factors for coronary heart disease

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Genetic Polymorphism of TLR5 and TLP6 in Iraqi Patients with Heart Failure Disease
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       In spite of the high rate of morbidity and mortality heart failure (HF) is common, and none of the medications are now entirely available for HF treatment. In addition to many environmental influences and clinical diseases, genetic factors may also contribute to the progression and development of HF.  In the current study, samples of blood were collected from 150 heart failure patients and 130 healthy controls. We evaluated the association of four single nucleotide polymorphisms (snps) of Toll-like receptors (TLR6 and TLR5) with (HF) susceptibility in the Iraqi population. In this work, (SNP) called Toll-like receptor 5 (rs5744168, rs2072493) and Toll-like receptor 6 (rs1039559, rs5743810) were employed. (PCR-RFLP) for snps

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Publication Date
Thu Jan 30 2020
Journal Name
Al-kindy College Medical Journal
Pattern of Congenital Heart Disease In Children Attending Central Teaching Pediatric Hospital, Baghdad
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Background: Congenital cardiac defects have a wide spectrum of severity in infants. About 30-40% of patients with congenital cardiac defects will be symptomatic in the 1st year of life, while the diagnosis was established in 60% of patients by the 1st month of age.

Objectives: To identify the occurrence of specific types of CHD among hospitalized patients and to evaluate of growth of patients by different congenital heart lesions.

Methods: A retrospective study, done on ninety-six patients (51 male and 45 female) with congenital heart disease (CHD) admitted to central teaching hospital of pediatrics, Baghdad from 1st September 2009 to 30

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Publication Date
Sat Dec 31 2016
Journal Name
Al-kindy College Medical Journal
Diagnostic Value Of Post Exercise Systolic Blood Pressure Response In Ischemic Heart Disease
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 Background: The normal decline in systolic blood pressure during recovery phase of treadmill exercise dose not occur in most patients with coronary artery disease, in others recovery values systolic blood pressure may even exceed the peak exercise value. Objectives: Treadmill exercise test parameters indicating the presence and extent of coronary artery disease have traditionally included such as exercise duration, blood pressure and ST-segment response to exercise. The three –minute systolic blood pressure ratio is another important indicator of presence and significance of coronary artery disease is useful and obtainable measure that can be applied in all patients who are undergoing stress testing for evaluation of suspected is

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Publication Date
Sun Dec 10 2023
Journal Name
Journal Of Applied Engineering And Technological Science (jaets)
The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review
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The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Ever

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