Application of Multilayer Perceptron (MLP) for Data Mining in Healthcare Operations
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The propensity for data mining methods in healthcare today is great because of the healthcare sector is abundant with information. Healthcare organizations produce and collect large volumes of information on daily basis. Because of the huge amount of information, study and analyses are too difficult. The use of data mining methods can help us to get precious information and regularities through medical databases, which can be used in future similar cases to save lives and also reduce the cost of health care services with the advanced collection of data. One of these cases is accouchement. The mechanism of accouchement is a natural process without the need for any interference. In some conditions, maybe the mother, baby or both of them are in hazard and need help and support. This help is provided by a caesarian section which saves the mother and baby. Nevertheless, we need to know when we should use surgery. This study explains the utilization of Multilayer Perceptron (MLP) with backpropagation (a supervised learning algorithm) in the determination of medical operation methods. We provide this by accumulating 80 pregnant women's information. The results show that Multilayer Perceptron (MLP) designed for this case study generates correct predictions for 95% test cases.
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