---

Introduction: The present study aims to compare the effectiveness of meta-cognitive intervention, along with Meichenbaum’s self-instructional approach, in the reduction of meta-cognitive beliefs of students suffering from test anxiety.

Materials & methods: This was a quasi-experimental study with pre-test and post-test scheme and control group. The statistical population consists of all female students with test anxiety from public high-schools in the city of Ilam and in the academic year 2014-2015. First, one of the girls' high schools in the city of Ilam was randomly chosen and the anxiety test was performed for 500 students. Then, 45 students who were diagnosed with test anxiety, were randomly distributed in two test and one control group. 8 sessions of therapeutic interventions was performed on the test groups, while the control group didn’t receive any intervention. Spielberger’s anxiety test and Wels’s meta- cognitive belief test was performed in order to identify people with test anxiety and to assess meta-cognitive beliefs, respectively. Data were analyzed using analysis of covariance.

Findings: The obtained results indicate that meta-cognitive therapy has been more effective compared to self-instructional and the control group in terms of the following components: “positive beliefs about worry”, “need for control”, and “negative beliefs about uncontrollability and danger of worry”, and no significant difference was observed between the self-instructional and control groups.Also,meta-cognitive therapy and self-instructional groups were more effective than the control group in terms of “cognitive self-consciousness”.

Discussion & Conclusions: Meta-cognitive therapy can be used to reduce meta-cognitive beliefs in people with test anxiety.

---

Introduction:  It is of utmost importance to predict cardiovascular diseases correctly. Therefore, it is necessary to utilize those models with a minimum error rate and maximum reliability. This study aimed to combine an artificial neural network with the genetic algorithm to assess patients with myocardial infarction and congestive heart failure.
 
Materials & Methods: This study utilized a multi-layer perceptron artificial neural network and a backpropagation algorithm combined with a genetic algorithm to assess the condition of two patients with cardiovascular diseases. The medical records of 497 patients with cardiovascular diseases at Ayatollah Golpayegani Hospital, Qom, Iran, were collected using a clustering sampling method. The data were analyzed using a Receiver Operating Characteristics Curve. Eventually, the data, including personal and clinical variables of patients (i.e., age, gender, dyspnea, blood pressure variations, and blood test results) were selected using sigmoid-transfer and tangent-sigmoid functions. Following that, the neural network was trained with 19 input neurons and 5 middle-layer neurons.
 
Findings: According to the results, a neural network with 5 middle-layer neurons has more precision, compared to other modes. Therefore, it is possible to predict myocardial infarction in the patients using this neural network with a minimum of 97.7% precision.
 
Discussion & Conclusions: An artificial neural network was combined with a genetic algorithm and proposed as a model to predict myocardial infarction in this study. Moreover, it was attempted to utilize important and cost-effective factors for cardiovascular diseases. As a result, the patients can be aware of their disease at the lowest cost.