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Background & objectives: Coronary heart disease (CHD) is an important medical disorder and one of the most common heart diseases worldwide, which causes disability and economic burden. The medical and research community is increasingly interested in computer-aided coronary heart disease diagnosis through the use of machine learning methods. This study aimed to diagnose coronary heart disease using a discriminant analysis algorithm in active elderly men. Methods: This analytical study was conducted on 351 patients of Ayatollah Kashani Hospital in Tehran. This work used discriminant analysis algorithm to diagnose coronary artery disease. Python software was used for data analysis. Results: The results showed that by using 14 characteristics as risk factors related to the subjects' laboratory, personal and lifestyle information. The discriminant analysis algorithm could distinguish healthy and sick people with 94.4% accuracy and 88.9% precision. Conclusion: The results of the present study showed that this system can probably be used as an effective and intelligent method along with other diagnostic methods by cardiologists to predict coronary artery disease. Also, new data mining methods can be effective in reducing invasive risks.

Background & objectives: Insulin-like growth factor -1 (IGF-1) has a variety of roles, but the abundance of scientific evidence indicates that it is a metabolic biomarker associated with physical fitness and health. The present study investigates the effect of eight weeks of polarized exercise training on serum GH / IGF-1- indices in active young men. Methods: In this double-blind experimental study, 20 young males were allocated randomly into polarized training group (N=10) and a control group (N=10). The polarized training group performed 80-70% of the main workout volume (30 minutes) with light to moderate with 50-60% reserve heart rate (RHR) intensity and the remaining 20-30% at 85-95% RHR intensity; in a way that they ran two periods consisting 3 repetitions of 15-30 seconds, with 30-60 seconds of active rest after each repetition and 3 minutes of active rest after each period. Blood samples were taken from all subjects in three stages, including: pre-test stages, 24 hours before the start of the post-test, and after 12 hours overnight fasting. Post-test samples were collected, one sample immediately after the first session and the another 48 hours after the end of the last exercise session. Results: The results of the present study showed that bipolar training significantly increased growth hormone and free IGF-I levels after one training session, and after eight-week bipolar training program. However, total IGF-1 levels decreased significantly after one exercise session and after eight-week bipolar exercise program. Also, no significant change was observed in IGFBP-3 and IGFBP-5 levels after one training session and eight-week training program. Acid-labile subunit levels did not change significantly after one training session, but decreased significantly after eight weeks of bipolar training. Conclusion: Based on the results of the present study, it seems that the use of bipolar exercises, training may be a good way to improve the hormonal function and assess the level of health and physical fitness of active young men.