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absence of medical diagnosis evidences, it is difficult for the experts to
opine about the grade of disease with affirmation. Generally many tests are
done that involve clustering or classification of large scale data. However
many tests could complicate the main diagnosis process and lead to the
difficulty in obtaining the end results, particularly in the case where many
tests are performed. This kind of difficulty could be resolved with the aid of
machine learning techniques. In this research, we present a comparative study
of different classification techniques using three data mining tools named
WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance
of different classification techniques for a set of large data. A fundamental
review on the selected techniques is presented for introduction purpose. The
diabetes data with a total instance of 768 and 9 attributes (8 for input and 1
for output) will be used to test and justify the differences between the
classification methods. Subsequently, the classification technique that has the
potential to significantly improve the common or conventional methods will be
suggested for use in large scale data, bioinformatics or other general
Many companies like credit card, insurance,
bank, retail industry require direct marketing. Data mining can help those institutes
to set marketing goal. Data mining techniques have good prospects in their target
audiences and improve the likelihood of response. In this work we have investigated
two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms.
The goal of this work is to predict whether a client will subscribe a term deposit.
We also made comparative study of performance of those two algorithms. Publicly
available UCI data is used to train and test the performance of the algorithms.
Besides, we extract actionable knowledge from decision tree that focuses to take
interesting and important decision in business area.
Background: Neurohumoral compensatory mechanisms play an important role in stabilizing the functional activity of patients with heart failure using the arms of autonomic nervous system. Orthostatic Hypotension (OH) is one of the most incapacitating symptoms of Cardiac Autonomic Dysfunction (CAD). OH can include sympathetic withdrawal which in turn leads to marked disability and deterioration of heart failure symptoms. Progressive Autonomic Dysfunction (AD) associated with progressive deterioration and impact on mortality of many diseases as hypertension, diabetes and other chronic diseases. The idea of using (OH) as a bed-side simple test expecting the risk of deterioration of cardiac function and furthermore on mortality open a gateway for preventive medicine and care to these group of patients. For more confidential prove, studying subjective and objective factors in heart failure patients became necessary to support these idea. Methods and Results: Sixty-Four patients with known history of heart failure were collected. All patients taking the fixed regiment of 4 drugs (diuretic, ACE inhibitor, Digitalis and B-blocker) in appropriate tolerated doses for two weeks prior to the study. History taking and all routine investigations were done for all patients. Grouping is based upon wither they have (OH) or not. Group-A found to have normal Bp response to standing; they were 24 patients (18 male and 6 female) of mean Age (45 ± 8 years). Group-B discovered to have significant (OH) and was 22 patients (16 males and 6 females) of mean Age (43 ± 4 years). The first Clinical and Echocardiographic examination was done and considered as a base-line characteristic. Then, a Call-back after 6 months for follow-up and second visit examination is recorded. Furthermore, every patient was advised to report