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 att

This study aims to investigate the features of stellar-gaseous kinematics and dynamics mass using scaling coefficient relationships (such as the Faber–Jackson relation (FJR)) of two samples of elliptical and lenticular galaxies. These two samples of 80 ellipticals and 97 lenticulars were selected from previous literature works. The Statistical Package for Social Sciences )SPSS( and Matrix Laboratory (MATLAB) program were used to find out the associations of multiple factors under investigation such as main kinematic properties of the gaseous-stellar (effective radius Re, surface density within the effective radius (Ʃeff) , stellar mass in the blue band (Mstar (B)), gas mass ( Mgas), dynamic (Mdyn) and baryonic (Mbar), supermassiv

Background: Among individuals who have a stenotic aortic valve, a precise assessment of aortic valve area is essential for clinical judgment. So far, no studies have been conducted to investigate and assess the role of the three dimensional echo-cardiography in the assessment of the valve stenosis. This study aims to compare and assess the precision of the measurement of the stenosis area of the aortic valve by 2D versus 3D echo-cardiography.

In this paper, construction microwaves induced plasma jet(MIPJ) system. This system was used to produce a non-thermal plasma jet at atmospheric pressure, at standard frequency of 2.45 GHz and microwave power of 800 W. The working gas Argon (Ar) was supplied to flow through the torch with adjustable flow rate by using flow meter, to diagnose microwave plasma optical emission spectroscopy(OES) was used to measure the important plasma parameters such as electron temperature (Te), residence time (Rt), plasma frequency (?pe), collisional skin depth (?), plasma conductivity (?dc), Debye length(?D). Also, the density of the plasma electron is calculated with the use of Stark broadened profiles

BACKGROUND: Carcinoma of urinary bladder is one of the most common malignancies worldwide and constitutes a major health problem. Multiple risk factors are associated with this tumor and its prognosis will depend on different clinicopathological parameters. Over expression of P53 protein and mutant Rb gene is associated with more aggressive clinical and histopathological features of the tumor such as advanced stage and higher grade. AIM: The immunohistochemical expression of Rb gene and P53 gene will be assessed through their protein products in transitional cell carcinoma (TCC) of the urinary bladder and then will be correlated with other well-known risk factors and prognostic parameters of bladder TCC, such as grading, tumor size, smoking

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise