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Machine Learning Techniques for Analyzing Survival Data of Breast Cancer Patients in Baghdad
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The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal component algorithm, The second kernel principal component algorithm, and The last is the faster ICA algorithm. Then the important features extracted in the three algorithms for features extraction will be entered into machine learning algorithms: The first K nearest neighbor algorithm, The second survival tree algorithm (or regression tree), and the last random survival forests algorithm.

Two criteria for comparing the best models to estimate survival have relied on the MSE and the C-Index. The best model for estimating and predicting survival is the use of the fastest ICA algorithm with the random survival forest algorithm that gave the lowest amount to MSE and the highest value to the C-Index. Accordingly, we recommend doctors and medical professionals in Iraq adopt this model to estimate survival for patients with breast cancer.

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Beran estimator using Nadaraya-Waston and Prestley-chao weights in estimating the conditional survival function of breast cancer patients
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This study includes the application of non-parametric methods in estimating the conditional survival function of the Beran method using both the Nadaraya-Waston and the Priestley-chao weights and using data for Interval censored and Right censored of breast cancer and two types of treatment, Chemotherapy and radiation therapy Considering age is continuous variable, through using (MATLAB)  use of the (MSE) To compare weights The results showed a superior weight (Nadaraya-Waston) in estimating the survival function and condition of Both for chemotherapy and radiation therapy.

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Publication Date
Sun Jan 03 2016
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Assessment of sociodemographic characteristics in a sample of breast cancer patients in Baghdad.
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Background: In Iraq, breast cancer is the most common type of malignancy among the Iraqi population in general. It accounts for approximately one third of the registered female cancers according to the latest Iraqi Cancer Registry.
Objectives: This study was conducted to assess the sociodemographic characteristics of patients with breast cancer in Baghdad.
Methodology: This cross sectional study that was conducted in Baghdad City during a three months period from January to March 2016. It was conducted at Al-Amal National Hospital for Cancer Management. The questionnaire form gathered info about sociodemographic characteristics including: age, gender, educational attainment, marital status, living arrangement, finical status, and d

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Two of (Turnbull) and (Generalization Turnbulls)non-parametric methods in estimating conditional survival function (applied study on breast cancer patients)
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   This research includes the application of non-parametric methods in estimating the conditional survival function represented in a method (Turnbull) and (Generalization Turnbull's) using data for Interval censored of breast cancer and two types of treatment, Chemotherapy and radiation therapy and age is continuous variable, The algorithm of estimators was applied through using (MATLAB) and then the use average Mean Square Error (MSE) as amusement  to the estimates and the results showed (generalization of Turnbull's) In estimating the conditional survival function and for both treatments ,The estimated survival of the patients does not show very large differences

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Detecting Textual Propaganda Using Machine Learning Techniques
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Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi National Journal Of Nursing Specialties
Impact of Psychological Distress in Women upon Coping with Breast Cancer: Coping with Breast Cancer
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Objective(s): To determine the impact of psychological distress in women upon coping with breast cancer.

Methodology: A descriptive design is carried throughout the present study. Convenient sample of (60) woman with breast cancer is recruited from the community. Two instruments, psychological distress scale and coping scale are developed for the study. Internal consistency reliability and content validity are obtained for the study instruments. Data are collect through the application of the study instruments. Data are analyzed through the use of descriptive statistical data analysis approach and inferential statistical data analysis approach.

Results: The study findings depict that women with breast cancer have experien

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Publication Date
Sun Jul 02 2017
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Biomarkers for evaluating response to chemotherapy in metastatic breast cancer patients
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Background: Although, different protocols of chemotherapy are recommended for the treatment of metastatic breast cancer, still response rates are variable.
Objectives: The aim of this study is to investigate the effects and correlation of different chemotherapy administered to metastatic breast cancer patients on serum levels of some biomarkers.
Patients and methods: Thirty metastatic breast cancer patients were enrolled in the study. The patients received different protocols of chemotherapy. Blood samples were taken from the patients before and after the last cycle of each protocol and from 20 healthy control and serum levels of biomarkers IL-6, leptin, CA 15-3 and p53 were estimated by Elisa.
Results: The mean serum levels of

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Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset
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Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

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Publication Date
Sun Oct 02 2016
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Assessment of early side effects of radiotherapy in breast cancer patients
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Background: Breast cancer is the commonest cancer in women. In radiotherapy practice, it comprises 25% of patient caseload. This makes understanding the breast irradiation toxicities of prime importance. Early radiation toxicities occur during treatment and up to six months after treatment finished.
Objectives: assessment of the early side effects of adjuvant external beam radiotherapy (EBRT) in breast cancer patients.
Patients and Methods: A cross sectional survey with analytic component conducted on 60 patients treated in the oncology teaching hospital of medical city from January to April 2016.
Results: The most prevalent toxicities were radiation dermatitis, fatigue, pain, sore throat, nausea, dysphagia,&

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of survival models to study determinants liver cancer
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Cancer is one of the dangerous diseases that afflict a person through injury to cells and tissues in the body, where a person is vulnerable to infection in any age group, and it is not easy to control and multiply between cells and spread to the body. In spite of the great progress in medical studies interested in this aspect, the options for those with this disease are few and difficult, as they require significant financial costs for health services and for treatment that is difficult to provide.

This study dealt with the determinants of liver cancer by relying on the data of cancerous tumours taken from the Iraqi Center for Oncology in the Ministry of Health 2017. Survival analysis has been used as a m

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