Comparing learning algorithms in neural network for diagnosing cardiovascular disease
This work provides an incremental comparison of existing algorithms for medical diagnosis, potentially aiding clinicians in selecting appropriate methods for cardiovascular disease detection.
The researchers compared nine neural network learning algorithms for diagnosing cardiovascular disease, finding that Lonberg-M performed best during training across all metrics, while OSS achieved maximum accuracy in testing, SCG had maximum transparency, and CGB had maximum sensitivity.
Today data mining techniques are exploited in medical science for diagnosing, overcoming and treating diseases. Neural network is one of the techniques which are widely used for diagnosis in medical field. In this article efficiency of nine algorithms, which are basis of neural network learning in diagnosing cardiovascular diseases, will be assessed. Algorithms are assessed in terms of accuracy, sensitivity, transparency, AROC and convergence rate by means of 10 fold cross validation. The results suggest that in training phase, Lonberg-M algorithm has the best efficiency in terms of all metrics, algorithm OSS has maximum accuracy in testing phase, algorithm SCG has the maximum transparency and algorithm CGB has the maximum sensitivity.