Abstract
Medical diagnosis focuses on previous knowledge and behavior of the disease. Sometimes it is very difficult for a doctor to predict the disease accurately and fast, based on his knowledge and experience. With the development of machine learning algorithms diagnosis solutions can be developed for many personalized medical problems. Artificial Neural Network (ANN) is going to become an essential part of medical diagnosis. ANN provides self-learning mechanism for complex problems like medical diagnosis. In this research, we are going to propose a novel Multi-Layer Pi-Sigma Neuron Model (MLPSNM) for medical diagnosis. This MLPSNM can diagnose different medical conditions. Proposed MLPSNM is trained by using standard BP algorithm. The bipolar sigmoidal function is used as an activation function. Normalization, PCA, and LDA preprocessing is used for data preprocessing. SVM model with LDA is also proposed in this research. For testing of proposed MLPSNM we select the UCI machine learning datasets.
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Burse, K., Kirar, V.P.S., Burse, A., Burse, R. (2019). Various Preprocessing Methods for Neural Network Based Heart Disease Prediction. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 851. Springer, Singapore. https://doi.org/10.1007/978-981-13-2414-7_6
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DOI: https://doi.org/10.1007/978-981-13-2414-7_6
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