Abstract: Many predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. To overcome this problem, predictive analytical techniques for heart stroke using machine learning model applied on given hospital dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke and the analysis of given dataset by supervised machine learning algorithm to capture several information’s like, variable identification, uni- variate analysis, bi-variate and multi-variate analysis, missing value treatments etc. The main objective is to predictive analytics model to diagnose heart stroke stages of patients. Additionally, discuss the performance from the given hospital dataset with evaluation of classification report and identify the confusion matrix. To propose a machine learning-based method to accurately predict the heart stroke by given attributes in the form of best accuracy from comparing supervise classification machine learning algorithms. Additionally, to compare and discuss the performance of various machine learning algorithms from the given healthcare department dataset with evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of GUI based the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.

Keywords: Dataset, python, Prediction of Accuracy result.

| DOI: 10.17148/IJIREEICE.2021.9434