Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system.

International audienceWell-designed medical decision support system (DSS) have been shown to improve health care quality. However, before they can be used in real clinical situations, these systems must be extensively tested, to ensure that they conform to the clinical guidelines (CG) on which they are based. Existing methods cannot be used for the systematic testing of all possible test cases. We describe here a new exhaustive dynamic verification method. In this method, the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used to build a decision tree from an exhaustive set of DSS input vectors and outputs. This method was successfully used for the testing of a medical DSS relating to chronic diseases: the ASTI critiquing module for type 2 diabetes

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes.</p> <p>Methods</p> <p>We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity.</p> <p>Results</p> <p>We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines.</p> <p>Conclusion</p> <p>Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used during the development of guidelines as a complementary source from practice-based knowledge. It can also be used as an evaluation tool for comparing a physician's therapeutic decisions with those recommended by a given set of clinical guidelines. The example we described showed that physician practice was in some ways ahead of the guideline.</p

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

تم عمل نظام دعم القرار السريري الآلي (CDSS) كنموذج جديد في الخدمات الطبية. بحيث يتم استخدام CDSSs لمساعدة الأخصائيين (الأطباء) في اتخاذ قراراتهم المحيرة. ولهذا السبب ، تم بناء DSS اعتمادًا على معرفة الأطباء وباستخدام استخراج البيانات لمساعدة خلية الازمة الطبية للسيطرة على جائحة فيروس COVID-19 ، وبشكل عام ، لتحديد الفئة من العدوى وتقديم علاج بروتوكول مناسب حسب أعراض المريض. في البداية لتشخيص المرض تم الاعتماد على ثلاث اعراض اولية هي ( الحمى, التعب والسعال الجاف) لمعرفة الشخص المصاب وعند تحديد أي من هذه الاعراض يتم تقسيم الاشخاص المصابين الى اربعة اصناف حسب مناعة الاشخاص ( اصابة طفيفة , اصابة عالية , اصابة شديدة جدا و طبيعي). وايضا يتم التشخيص باستخدام عاملين هما ( عمر المريض و الامراض المزمنة للمريض مثل السكر ومشاكل القلب وضغط الدم ) ثم يتم تقدير حالة المصاب حيث توجد ستة مستويات للاشخاص المصابين بفيروس كورونا 2019 وتحتاج الى عناية حسب حالة المصاب. عندما يكون الفحص موجب واعتمادا على عمر المريض والامراض المزمنة يتم تحديد في أي مستوى من المستويات الستة يكون المريض حسب الاعراض . وبذلك يتم تحديد درجة حالة المريض من الدرجات الاربع ثم يتم اقتراح اربعة بروتوكولات للعلاج ويتم اختيار الانسب حسب اختيار الاطباء وايضا يوفر النظام معلومات كاملة عن الوقاية وتجنب الوباء واخيرا يتم ارسال ايميل يحتوي جميع المعلومات من مركز السيطرة لللاشخاص المسؤولين . تم اعتماد خوارزمية C4.5  في شجرة اتخاذ القرار لبناء هذا التطبيق.                                                                                                                                              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree

The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation

One of the datasets used to predict heart disease is UCI dataset. unfortunately, the dataset contains missing data. the missing data dramatically affects the performance of the backpropagation classification method. One of the techniques used to handle missing data is feature selection. This study compares the ReliefF and the C4.5 algorithm in feature selection to handle missing data. The results of these algorithms are applied to the classification of heart disease using the Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are an accuracy of 82.653%, a precision of 82.7%, and a recall of 82.7%. The performance results of the C4.5 and backpropagation are an accuracy of 80.61%, a precision of 80.4%, and a recall of 80.6%. Based on the results it can be concluded that the ReliefF gives better performance results on backpropagation than the performance results of the C4.5. Although, the results of C4.5 are below ReliefF but the results are quite satisfactory because of the accuracy, precision and recall results obtained above 80%. This shows that ReliefF and C4.5 can select features that affect the UCI heart disease patient dataset

A New Efficiency Improvement of Ensemble Learning for Heart Failure Classification by Least Error Boosting

Heart failure is a very common disease, often a silent threat. It's also costly to treat and detect. There is also a steadily higher incidence rate of the disease at present. Although researchers have developed classification algorithms. Cardiovascular disease data were used by various ensemble learning methods, but the classification efficiency was not high enough due to the cumulative error that can occur from any weak learner effect and the accuracy of the vote-predicted class label. The objective of this research is the development of a new algorithm that improves the efficiency of the classification of patients with heart failure. This paper proposes Least Error Boosting (LEBoosting), a new algorithm that improves adaboost.m1's performance for higher classification accuracy. The learning algorithm finds the lowest error among various weak learners to be used to identify the lowest possible errors to update distribution to create the best final hypothesis in classification. Our trial will use the heart failure clinical records dataset, which contains 13 features of cardiac patients. Performance metrics are measured through precision, recall, f-measure, accuracy, and the ROC curve. Results from the experiment found that the proposed method had high performance compared to naïve bayes, k-NN,and decision tree, and outperformed other ensembles including bagging, logitBoost, LPBoost, and adaboost.m1, with an accuracy of 98.89%, and classified the capabilities of patients who died accurately as well compared to decision tree and bagging, which were completely indistinguishable. The findings of this study found that LEBoosting was able to maximize error reductions in the weak learner's training process from any weak learner to maximize the effectiveness of cardiology classifiers and to provide theoretical guidance to develop a model for analysis and prediction of heart disease. The novelty of this research is to improve original ensemble learning by finding the weak learner with the lowest error in order to update the best distribution to the final hypothesis, which will give LEBoosting the highest classification efficiency. Doi: 10.28991/ESJ-2023-07-01-010 Full Text: PD