Adaptive neuro-fuzzy inference system for prediction of surgery time for ischemic stroke patients

Abstract is compulsory. First sentence describes the nature or the background information on the fie ld of study. Subsequent sentences provide the problem statement or objectives and scope of the research. Ne xt sentences e xpla in the methods and materia ls used in the work. Main results and important findings are then highlighted. Finally, a summary of conclusions is put forth. Length of abstract can be proportional to the length of the article.Keywor ds:Keyword 1,keyword 2,number of keywords is usually 3-7, but more is allowed if deemed necessaryAbstrac t:W ith the advent of machine learning techniques, creation and utilization ofpred iction models for diffe rent med ical p rocedures including prediction of diagnosis, treatment and recovery of different med ical conditions has become the norm. Recent studies focus on the automation of infarct ion volu me growth rate prediction by the utilization of machine learn ingtechniques.These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fu zzy Inference System that can determine when a stroke patient should undergo Decompressive Hemic raniectomy. The second infarction volume gro wth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained mode ls, act as inputs to this system. While the init ial predict ion model, that wh ich predicts the second infarction volume growth rate is adopted from an earlie r model, we propose the later model in this paper. Three Machine Learn ing techniques -Support Vector Machine, Art ificia l Neura l Network and Adaptive Neuro Fuzzy Infe rence System with and without the feature reduction technique of Princip le Co mponent Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its ru les and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic