# An Optimized Brain-Based Algorithm for Classifying Parkinson's Disease
###### Abstract  
During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm's performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson's Disease audio dataset taken from UCI Machine Learning Repository. [PERSON]'s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson's Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%. Keywords:extreme learning machine; artificial neural networks; bat algorithm; optimization algorithms; parkinson's disease +
Footnote †: journal: MedicalDespite the great success of artificial neural networks, its proper design and its successful implementation to reach acceptable results is not a simple task. The procedure of ANNs is regulated by a set of parameters that demand a previous configuration. In fact, selecting the appropriate parameter setting is crucial for efficient algorithm performance, and therefore to the effective finding of outstanding solutions [16]. Furthermore, there is no general optimal artificial neural network configuration. Hence, the effects of an optimal initial configuration can considerably vary from problem to problem and also between different instances of the same problem. This hard configuration task is typically left to the modeler who has to decide based on the features of the problem and the available parameter tuning knowledge. This task requires a significant amount of expertise. This open challenge has led to new trends trying to modify ANNs in order to minimize the number of parameters, and thus to avoid adjusting their values. That is how the Extreme Learning Machine (ELM) was designed. In [17], ELM was proposed as a kind of artificial neural network which is only composed by an input layer, an output node, and one or more layers of neurons. Unlike a conventional artificial neural network, the ELM proposes no tuning of the parameters of the hidden nodes: input weights and hidden layer biases, because they are arbitrarily assigned. This design tends to provide an efficient generalization performance at breakneck learning speed [18]. However, to reach this performance, ELM sacrifices precise and accurate in results. The trade-off between high-quality solutions and the fast resolution process is an issue that should be covered. In this work, we use the Bat Algorithm (BA) to enhance the yield of the extreme learning machine during the learning phase. BA is a metaheuristic inspired by the echolocation feature of microbats [19], and we already had fruitfully proved in optimization problems [20; 21]. The main idea is to automatically adjust the random parameters of the ELM for boosting to the neural network to achieve better performance. Next, we use the models generated by the improved ELM as an offline comparison resource and we analyze them in terms of accuracy-loss. There are two objectives in conflict: to maximize accuracy and minimize loss. The balance between these measures allows us to find the best model for the test phase of the ELM.