Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods

Optimum Median Filter Based on Crow Optimization Algorithm

يُقترح مرشح متوسط ​​جديد يعتمد على خوارزميات تحسين الغراب (OMF) لتقليل ضوضاء الملح والفلفل العشوائية وتحسين جودة الصور ذات اللون الرمادي والملونة . الفكرة الرئيسية لهذا النهج هي أن أولاً ، تقوم خوارزمية تحسين الأداء بالكشف عن وحدات البكسل الخاصة بالضوضاء ، واستبدالها بقيمة وسيطة مثالية تبعًا لدالة الأداء. أخيرًا ، تم استخدام نسبة القياس القصوى لنسبة الإشارة إلى الضوضاء (PSNR) ، والتشابه الهيكلي والخطأ المربع المطلق والخطأ التربيعي المتوسط ​​لاختبار أداء المرشحات المقترحة (المرشح الوسيط الأصلي والمحسّن) المستخدمة في الكشف عن الضوضاء وإزالتها من الصور. يحقق المحاكاة استنادًا إلى MATLAB R2019b والنتائج الحالية التي تفيد بأن المرشح المتوسط ​​المحسّن مع خوارزمية تحسين الغراب أكثر فعالية من خوارزمية المرشح المتوسط ​​الأصلية ومرشحات لطرق حديثة ؛ أنها تبين أن العملية المقترحة قوية للحد من مشكلة الخطأ وإزالة الضوضاء بسبب مرشح عامل التصفية المتوسط ​​؛ ستظهر النتائج عن طريق تقليل الخطأ التربيعي المتوسط ​​إلى أدنى أو أقل من (1.5) ، والخطأ المطلق للتساوي (0.22) ,والتشابه الهيكلي اكثر من ( 95%) والحصول على PSNR أكثر من 45dB).) وبنسبة تحسين ( 25%) .          A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%)

Machine Learning Architecture for Heart Disease Detection: A Case Study in Iraq

In recent years, the amount of data has been increased dramatically, driven by many real-world fields such as marketing, learning, social media, multimedia, medicine…etc. Because of that, data mining algorithms have extensively used on these data to serve as one of the newest data modeling and analytical tools, by which, a knowledge-rich environment can be generated and decision-making can be improved. Data mining tools can be employed for reducing these tests and predicting future trends by valuable information-driven decisions.  There are two categories of data mining algorithms: descriptive and predictive. The rules of clustering, association, summarization, and sequence discovery will be associated with descriptive type. On the other hand, predictive type will compromise classification, regression and time series analysis rules. In this paper, a study have been presented for helping specialists and physicians in Iraq to investigate heart problems via (Weka 3.8.3) software focusing on four data mining classification techniques (1BK, J48, Naïve Bayes and REPTREE). The predictive precision tests, the ROC curve, and the AUC value are calculated using a compiled dataset that have been obtained from the hospital of Ibn al-Bitar and the hospital of Baghdad medical city. The performance of the J48 technique (94.5%) indicates optimum performance based on SMO no performance factor of Baghdad medical city.

A REVIEW PAPER: ANALYSIS OF WEKA DATA MINING TECHNIQUES FOR HEART DISEASE PREDICTION SYSTEM

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is a cardiovascular disease that causes death. Health problems are enormous in this recent situation because of the prediction and the classification of health problems in different situations. The data mining area included the prediction and identification of abnormality and its risk rate in these domains. Today the health industry holds hidden information essential for decision-making. For predicting heart problems, data extraction algorithms like K-star, J48, SMO, Naïve Bayes, MLP, Random Forest, Bayes Net, and REPTREE are used for this study (Weka 3.8.3) software. The results of the predictive accuracy, the ROC curve, and the AUC value are combined using a standard set of data and a collected dataset. By applying different data mining algorithms, the patient data can be used for diagnosis as training samples. The main drawbacks of the previous studies are that they need accurate and the number of features. This paper surveys recent data mining techniques applied to predict heart diseases. And Identifying the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as high blood cholesterol, diabetes, smoking, poor diet, obesity, hypertension, stress, etc. Data mining functions and techniques are used to identify the level of risk factors to help the patients in taking precautions in advance to save their life

A REVIEW PAPER: ANALYSIS OF WEKA DATA MINING TECHNIQUES FOR HEART DISEASE PREDICTION SYSTEM

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is a cardiovascular disease that causes death. Health problems are enormous in this recent situation because of the prediction and the classification of health problems in different situations. The data mining area included the prediction and identification of abnormality and its risk rate in these domains. Today the health industry holds hidden information essential for decision-making. For predicting heart problems, data extraction algorithms like K-star, J48, SMO, Naïve Bayes, MLP, Random Forest, Bayes Net, and REPTREE are used for this study (Weka 3.8.3) software. The results of the predictive accuracy, the ROC curve, and the AUC value are combined using a standard set of data and a collected dataset. By applying different data mining algorithms, the patient data can be used for diagnosis as training samples. The main drawbacks of the previous studies are that they need accurate and the number of features. This paper surveys recent data mining techniques applied to predict heart diseases. And Identifying the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as high blood cholesterol, diabetes, smoking, poor diet, obesity, hypertension, stress, etc. Data mining functions and techniques are used to identify the level of risk factors to help the patients in taking precautions in advance to save their life

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population