Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

Breast cancer is the most prevalent malignancy among women worldwide, in Iraq it ranks the first among the population and the leading cause of cancer related female mortality. This study is designed to investigate the correlations between serum and tissue markers in order to clarify their role in progression or regression breast cancer. Tumor Markers are groups of substances, mainly proteins, produced from cancer cell or from other cells in the body in response to tumor.  The study was carried out from April 2018 to April 2019 with total number of 60 breast cancer women. The blood samples were collected from breast cancer women in postoperative and pretherapeutic who attended teaching oncology hospital of the medical city in Baghdad and

Diamond-like carbon, amorphous hydrogenated films forms of carbon, were pretreated from cyclohexane (C6H12) liquid using plasma jet which operates with alternating voltage 7.5kv and frequency 28kHz. The plasma Separates molecules of cyclohexane and Transform it into carbon nanoparticles. The effect of argon flow rate (0.5, 1 and 1.5 L/min) on the optical and chemical bonding properties of the films were investigated. These films were characterized by UV-Visible spectrophotometer, X-ray diffractometer (XRD) Raman spectroscopy and scanning electron microscopy (SEM). The main absorption appears around 296, 299 and 309nm at the three flow rate of argon gas. The value of the optical energy gap is 3.37, 3.55 and 3.68 eV at a different flow rate o

Oil price forecasting has captured the attention of both researchers and academics because of the unique characteristics of crude oil prices and how they have a big impact on a lot of different parts of the economic value of the product. As a result, most academics use a lot of different ways to predict the future. On the other hand, researchers have a hard time because crude oil prices are very unpredictable and can be affected by many different things. This study uses support vector regression (SVR) with technical indicators as a feature to improve the prediction of the monthly West Texas Intermediate (WTI) price of crude oil. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) measur