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Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently, diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a...
The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends...
Word prediction is an applicable task for medical purposes and it can be done by analyzing brain's activities. Functional Magnetic Resonance Imaging (fMRI) is a technique for obtaining 3D images, related to the neural activity of brain through time. By subtracting fMRI images, which are captured consecutively, brain's operation can be detected. In this paper, a novel approach, based on machine learning...
A large number of text data are regularly published in social networks and the media. Processing and analysis of such information is an highly required direction. This paper focuses on the way to use the entropy measure when dealing with big volumes of text data in classification. The used entropy measure stands for algorithm quality criteria when defining a class in a set of data. The work also features...
Currently, many educational institutions are highly oriented to improve the quality of education and students? learning achievement-examination result. To fulfil such intention, predicting students? performance by analyzing their learning behavior is one of the best way can be taken into account. Once the performance was predicted, it will be easy for teachers, school authority or other related parties...
The second largest cause of death in Palestine is Cancer at a rate 12.4% of all deaths. Predicting the survivability of a disease is one of the most interesting purposes of developing a medical data mining applications. This paper applies two classification models (Rule Induction and Random Forest) on the Gaza Strip 2011 cancer patient's dataset, to predict the survivability of cancer patients. The...
Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew members. The fateful incident still compel the researchers and analysts to understand what can have led to the survival of some passengers and demise of the others. With the use of machine learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set, the research...
In today's competitive environment, having a customer oriented approach is inevitable for organizations. At this point, in order to achieve customer satisfaction, customer relationship management (CRM) targets to provide products and services which meet customer expectation. Data which is collected about customers is an important source to determine their needs. Therefore, analysis is made to determine...
In major colleges and universities, in order to mobilize students enthusiasm for studying and participating in extracurricular activities, all colleges make an evaluation on students comprehensive quality and set different rewards regulations for the various level. The main way is to provide financial incentives, they distribute scholarship for students of meeting requirements. The Decision Tree algorithm...
In Machine Learning, we often encounter instances of imbalanced data which occur whenever there is an unequal representation in the classification categories. New found interest in Machine Learning has made its usage ubiquitous. Its applications encompass a wide plethora of scenarios ranging from Business and Banking to Bioinformatics and Psychology. These problems are often characterized by imbalanced...
There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
A question that is often arose on career management is how to choose potential employees to become chief and achieve performance target based on employees' historical data. This research attempts to answer the question and tries to determine what factors can affect an employee to become a chief and capable to achieve performance target. To address the question, this study adopts predictive modelling...
The number of kidney disease patients, one of the worldwide public health problems, has been increased yearly. Due to the high possibility of death within a short period of time, a patient must be hospitalized and appropriately cured since the first day of being diagnosed as stage 3. This is due to the fact that the patient's stage progression depends pretty much on medical history and treatment....
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques,...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more...
The amount of fat on the surroundings of the heart is correlated to several health risk factors such as carotid stiffness, coronary artery calcification, atrial fibrillation, atherosclerosis, cancer incidence and others. Furthermore, the cardiac fat varies unrelated to the overall fat of the subject, and, therefore, it reinforces the quantitative analysis of these adipose tissues as being essential...
Intrusion Detection System have been successful to prevent attacks on network resources, but the problem is that they are not adaptable in cases where new attacks are made i.e. they need human intervention for investigating new attacks. This paper proposes the creation of predictive intrusion detection model that is based on usage of classification techniques such as decision tree and Bayesian techniques...
Prediction is a challenging task and that too for weather is even more complex, dynamic and mind-boggling. Weather prediction poses right from the ancient times as a big herculean task, because it depends on various parameters to predict the dependent variables like temperature, rainfall, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the...
The growth of academic data size in higher education institutions increases rapidly. This huge volume of data collection from many years contains hidden knowledge, which can assist the improvement of education quality and students performance. Students' performance is affected by many factors. In this study, the data used for data mining were students' personal data, education data, admission data,...
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