Abstract
We see that the diagnosis of disease is very difficult these days and for ordinary doctors even it becomes perplexing to handle the situation we use the combined power of artificial neural network and Regression analysis. In this paper we use certain new techniques by combining the capabilities of statistical analysis and Artificial Neural Network. We also give a relation of BMI with age by using data mining and use that for our analysis. Here in our paper we also identify placebo i.e. a patient actually does not have the symptoms but because he/she thinks that they have the disease and hence tells to a doctor that he/she has the disease. We identify a parameter called α and theoretically justify that using it we can identify which one is placebo. It is distinguished that probability and fuzzy logic are independent but considering one thing that occurrence of disease is based on probability i.e. two outcomes the disease may happen or may not happen but evaluation of symptoms is essentially fuzzy i.e. “Feeling VERY cold”or “Feeling EXTREMELY cold”. So we intimately connect these two different concepts. Hence we use a membership function in our paper to calculate probabilities.
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Das, S., Sanyal, M.K., Datta, D. (2018). Advanced Diagnosis of Deadly Diseases Using Regression and Neural Network. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_29
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DOI: https://doi.org/10.1007/978-981-13-1343-1_29
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