Data Mining Techniques for Iraqi Biochemical Dataset Analysis

        يهدف هذا البحث إلى تحليل ومحاكاة بيانات تحاليل الكيمياء الحيوية الحقيقية للكشف عن العلاقات فيما بين التحاليل ، وكيف يؤثر كل منها على الآخرين. تم الحصول على البيانات من مختبر الكيمياء الحيوية العراقي الخاص. كذلك فإن هذه البيانات لها أبعاد عديدة ذات معدل مرتفع من القيم الخالية وأعداد كبيرة من المرضى. بعد ذلك ، تم تطبيق العديد من التجارب على هذه البيانات بدءًا بتقنيات غير خاضعة للرقابة مثل التجمعات الهيكلية وك-الوسائل ، ولكن النتائج لم تكن واضحة. ثم تم تنفيذ خطوة المعالجة المسبقة ، لجعل مجموعة البيانات قابلة للتحليل من خلال تقنيات خاضعة للإشراف مثل التحليل التمييزي الخطي (LDA) ، وشجرة التصنيف والانحدار (CART) ، والانحدار اللوجستي (LR) ، و ك-اقرب جار (K-NN) ، و نايف بايز ( NB) ، وتقنيات آلة ناقل الدعم (SVM). يعطي CART نتائج واضحة بدقة عالية بين الخوارزميات الستة الخاضعة للإشراف. من الجدير بالذكر أن خطوات المعالجة المسبقة تتطلب جهودًا ملحوظة للتعامل مع هذا النوع من البيانات ، نظرًا لأن مجموعة البيانات الخالصة بها العديد من القيم الصفرية بنسبة 94.8٪ ، ثم تصبح 0٪ بعد تحقيق خطوات المعالجة المسبقة. ثم ، من أجل تطبيق خوارزمية CART ، تم افتراض العديد من الاختبارات المحددة كفئات. قرار اختيار الاختبارات التي تم افتراضها على أنها فئات كانت تعتمد على دقتها المكتسبة. وبالتالي ، تمكين الأطباء من تتبع وربط نتائج الاختبارات مع بعضها البعض ، مما يوسع تأثيرها على صحة المرضى.This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB), and Support Vector Machine (SVM) techniques. CART gives clear results with high accuracy between the six supervised algorithms. It is worth noting that the preprocessing steps take remarkable efforts to handle this type of data, since its pure data set has so many null values of a ratio 94.8%, then it becomes 0% after achieving the preprocessing steps. Then, in order to apply CART algorithm, several determined tests were assumed as classes. The decision to select the tests which had been assumed as classes were depending on their acquired accuracy. Consequently, enabling the physicians to trace and connect the tests result with each other, which extends its impact on patients’ health

Peningkatan Kemampuan Pemecahan Masalah Pada Materi Sistem Persamaan Linear Dua Variabel Melalui Aplikasi Pembelajaran Blended Learning Berbasis Edmodo

This study aims to determine the effect of the Blended Learning learning model based on the Edmodo application on the mathematical problem solving ability of students in class VIII of SMPN 2 Indrajaya, Pidie Regency. The research method used is quasi-experimental using a pretest-posttest research design. The population of this study were all eighth grade students of SMP Negeri 2 Indrajaya, Pidie Regency. The samples used in this study were two classes, namely class VIII.2 which amounted to 30 students and class VIII.3 which amounted to 27 students. The data collection technique used is a test sheet in the form of an essay to measure students' mathematical problem solving abilities. The hypothesis test uses a one-sided T-test, namely the right-hand T-test. The results showed that the average mathematical problem solving ability of students in the experimental class was 28.1583 while the average mathematical problem solving ability of students in the control class was 21.6690. After the average difference test was carried out, it was obtained that tcount ≥ t table 7,56≥1,67 so that Ha was accepted, which means that the mathematical problem solving ability of students who were taught with the Edmodo application-based Blended Learning learning model was better than students who studied with the conventional model

Most Common Dental Complications in Chronic Disease Patients

The term "oral health" describes the conditions of the teeth, gums, and overall oral-facial system, which enables people to chew, speak, and smile. Some of the most common oral disorders are cavities (tooth decay), gum disease (periodontitis), and oral cancer. In the past year, about 40% of people stated feeling oral pain, and by the time they reach 34 years, more than 80% of individuals will have suffered from at least one cavity. so we are putting our hands to explore the most common dental complications in chronic diseases patient

Implementasi Algoritma K-Nearest Neighbor (KNN) Dalam Memprediksi Indeks Kemiskinan

Kemisikinan merupakan masalah yang harus dihadapi oleh Pemerintah, kemiskinanjuga dapat berpengaruh terhadap tindak keriminal. Oleh sebab itu diperlukan perhatian khususuntuk menekan angka kemiskinan, Pemerintah telah melakukan upaya dalam menekan angkakemiskinan, diantaranya dengan memberikan berbagai macam bantuan kepada rakyat miskinberdasarkan data yang diperoleh. Selain itu Pemerintah juga perlu memperhatikan indekskemiskinan pada setiap provinsi, hal ini bertujuan untuk mengetahui informasi indekskemiskinan dalam waktu tertentu. Penelitian ini membahas tentang prediksi indeks kemiskinanmenggunakan metode K-Nearest Neighbor (KNN) dalam memprediksi indeks kemiskinandisetiap Provinsi dengan menggunakan data yang diperoleh dari Badan Pusat Statistik (BPS)

An Insight into the Lynch Syndrome: Retrospective Study of the Pattern of Presentation and Management of Lynch Syndrome in Pakistan

Introduction: The primary objective of this study was to evaluate the baseline characteristics of Lynch syndrome (LS). Furthermore, the study aimed to evaluate overall survival (OS) among patients with LS. Materials and Methods: This was a retrospective study of colorectal cancer patients registered from January 2010 to August 2020 with an immunohistochemical diagnosis of LS. Results: A total of 42 patients were assessed. The mean age at presentation was 44 years, with male predominance (78%). Demographic preponderance was from the North of Pakistan (52.4%). The family history was positive in 32 (76.2%) patients. The colonic cancer distribution was 32 (76.2%) on the right side. Most of the patients presented with Stage II disease (52.4%), and the common mutations were MLH1 + PMS2 16 (38.1%) followed by MSH2 + MSH6 9 (21.4%). The 10-year OS was found to be 88.1%. However, the OS was 100% post pancolectomy. Conclusion: LS is prevalent in the Pakistan population, especially in the North of Pakistan. Clinical presentation and survivals are similar to the Western population

Comparative study between aortic valve replacement through full sternotomy versus mini-sternotomy

Background: The superiority of minimally invasive aortic valve replacement (AVR) over the standard approach is the subject of ongoing research. The aim of this study was to compare the outcomes of AVR through full sternotomy versus mini-sternotomy. Methods:  We included 60 patients who had AVR; 30 patients underwent AVR through J- or T-shaped mini-sternotomy, and 30 patients had a full sternotomy. We included patients who had isolated AVR and excluded patients who had a concomitant cardiac procedure, redo surgery, or those who needed annular dilatation. All patients had aortic and right atrial cannulation for cardiopulmonary bypass. Study endpoints were operative times, postoperative complications and duration of ICU and hospital stays. Results: There were no differences between the two groups preoperatively. Cardiopulmonary bypass time was longer in the mini-sternotomy group (median: 100 (range: 65- 170) vs. 85 (55-160) min, respectively; p= 0.024). Operative time was non-significantly longer in the mini-sternotomy group 5 (4-6) hours vs. 4.5 (4-6) hours in the full sternotomy group (p=0.62). Ventilation time was 10 (4- 50) hours in the mini-sternotomy group vs. 14 (8- 45) hours in the full sternotomy group (p<0.001). ICU stay was shorter in the mini-sternotomy group (2 (1-6.5) vs. 2.5 (1-7) days, respectively, p= 0.014). The total mediastinal drainage was 100 (50 400) ml in the mini-sternotomy group vs. 275 (50- 1000) ml in the full sternotomy group (p= <0.001). There was no difference in wound infection (p= 0.35), tamponade (p˃0.99), and hemothorax (p˃0.99) between both groups. Conclusion: Mini-sternotomy AVR had longer cardiopulmonary bypass times; however, there were no differences in the postoperative complications compared to the full sternotomy approach. Mini-sternotomy could be a safe alternative approach to the full median sternotomy for aortic valve replacement

Analysis of a high pressure diesel spray at high pressure and temperature environment conditions

This paper illustrates the results of an experimental characterization of a high pressure diesel spray injected by a common rail (CR) injection system both under non-evaporative and evaporative conditions. Tests have been made injecting the fuel with a single hole injector having a diameter of 0.18 mm with L/D=5.56. The fuel has been sprayed at 60, 90 and 120 MPa, with an ambient pressure ranging between 1.2 to 5.0 MPa. The spray evolution has been investigated, by the Mie scattering technique, illuminating the fuel jet and acquiring single shot images by a CCD camera. Tests under non-evaporative conditions have been carried out in an optically accessible high pressure vessel filled with inert gas (N2) at diesel-like density conditions. The instantaneous fuel injection rate, obtained with a time resolution of 10 microseconds, has been also evaluated by an AVL Fuel Meter working on the Bosch Tube principle. Tests for the evaporative conditions have been conducted on a crank-case scavenged single cylinder 2-stroke direct injection Diesel engine at the rotational speed of 500 rpm. The engine provides a wide optical access and the gas velocity within the combustion chamber is low enough to assume that the fuel is injected under quiescent conditions as those reproduced for the experiments under high density gas chamber. Spray penetration and cone angle have been estimated at the same operative conditions as for the non-evaporative ones. Results have showed that the tip penetration, obtained by digital post-processing of the spray image sequence, increases with the injection time under non-evaporative conditions whereas, under evaporative conditions, it reaches a maximum early during the injection and remains constant or slightly decreases at later time up to the start of combustion. The cone angle, estimated under evaporative conditions, has given a decreasing profile along the injection interval. Applying the jet theory to a simplified model of fuel spray, the evaporated fuel mass has been estimated at the same gas density as that under non evaporative tests

Prevalence of obesity in school-going children of Karachi.

Background: Obesity is an emerging problem in Pakistan. The authors sought to determine prevalence of obesity and malnutrition in school-going children, from grades 6(th) to 8(th) of different schools of Karachi and assess associations that affect the weight of the children. Methodology/Principal Findings: A cross sectional Study Design with children studying in grades 6(th) to 8(th) grade, in different schools of Karachi. We visited 10 schools of which 4 consented, two subsidized government schools and two private schools. A questionnaire was developed in consultation with a qualified nutritionist. Height and weight were measured on calibrated scales. A modified BMI criterion for Asian populations was used. Data was collected from 284 students. Of our sample, 52% were found to be underweight whereas 34% of all the children were normal. Of the population, 6% was obese and 8% overweight. Of all obese children, 70% belonged to the higher socio-economic status (SES) group, while of the underweight children, 63.3% were in the lower SES. Amongst obese children in our study, 65% ate meat every day, compared to 33% of normal kids. Conclusion: Obesity and undernutrition co-exist in Pakistani school-children. Our study shows that socio-economic factors are important since obesity and overweight increase with SES. Higher SES groups should be targeted for overweight while underweight is a problem of lower SES. Meat intake and lack of physical activity are some of the other factors that have been highlighted in our study.

Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible open-source automated machine learning framework encapsulating common prediction problems in the health domain and allows users to build predictive models with their own data. This system relies on two components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized data structure for electronic health systems -- and several AUTOML frameworks for automated feature engineering, model selection, and tuning. We augment these components with an adaptive data assembler and comprehensive data- and model- auditing capabilities. We demonstrate our framework via 5 prediction tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human competitiveness, flexibility in problem definition, extensive feature generation capability, adaptable automatic data assembler, and its usability

Wearable artificial intelligence for anxiety and depression: A scoping review

Background: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of AI and wearable devices (wearable artificial intelligence (AI)) have been exploited to provide mental health services. Objective: The current review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods: We searched 8 electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar). Then, we checked studies that cited the included studies, and screened studies that were cited by the included studies. Study selection and data extraction were carried out by two reviewers independently. The extracted data were aggregated and summarized using the narrative synthesis. Results: Of the 1203 citations identified, 69 studies were included in this review. About two thirds of the studies used wearable AI for depression while the remaining studies used it for anxiety. The most frequent application of wearable AI was diagnosing anxiety and depression while no studies used it for treatment purposes. The majority of studies targeted individuals between the ages of 18 and 65. The most common wearable devices used in the studies were Actiwatch AW4. The wrist-worn devices were most common in the studies. The most commonly used data for model development were physical activity data, sleep data, and heart rate data. The most frequently used dataset from open sources was Depresjon. The most commonly used algorithms were Random Forest (RF) and Support Vector Machine (SVM). Conclusions: Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals as a pre-screening assessment of anxiety and depression. Further reviews are needed to statistically synthesize studies’ results related to the performance and effectiveness of wearable AI. Given its potential, tech companies should invest more in wearable AI for treatment purposes for anxiety and depression