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Determination of Heart Disease Based on Analysis of Patient Statistics using the Fuzzy C-means Clustering Algorithm

The statistical characteristics of patients have a great influence on determining the likelihood of heart disease. To determine the disease in medical diagnostics, statistical methods are most often used Data Mining, which with large amounts of information and complex relationships can give more accurate estimates, especially with a large number of similar characteristics. In this paper, we consider the task of clustering data to determine the likelihood of heart disease for patients with similar characteristics. To solve the problem, the Fuzzy C-means method with normalization was used, and Python and the Pandas library were chosen to implement the algorithm. The developed software module allows visualization of the algorithm. This system also supports downloading and uploading data with the service according to safety requirements of data.