A New-fangled Classification Algorithm for Medical Heart Diseases Analysis using Wavelet Transforms

Background In this article, the Mixed Mode Database Miner (MMDBM) algorithm is introduced for the classification of data. This algorithm depends on the decision tree classifier, which handles the numerical and categorical attributes. For the experimental analysis in a well-explored heart disease data set collected from the UCI Repository. Aims Understanding the fundamentals of every classification method and how to apply them to CUDA is the aim of this study. After reviewing the literature, the approach that is most suited is selected for implementing the suggested algorithm in the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification. Objective The main objective of the manuscript is to identify the early stage of heart attack that was caused either by smoking, smoking with tobacco, or non-smoking. Additionally, this study aims to check the validity of the MMDM classifier along with the discrete wavelet decomposition. From this experimental analysis, we observed that the use of the wavelet technique in the MMDBM algorithm provides better and more accurate results for data classification. Methods In the modern digital world today, data has a major impact on everyone’s life. Every database contains a lot of information hidden either in the form of numerical data, characteristic data, or a mixture of both. Moreover, to accurately decode every dataset, a fast and efficient classifier is essential.. Moreover, by using this hybrid technique based on both MMDBM and Wavelet processes, it compresses the data with minimum storage capacity. Results The experimental results are compared with the classified data to wavelet data output. These results prove that our presented technique is more prominent and robust in an analysis of heart diseases. Conclusion This algorithm is based on a decision tree classifier and tested on a heart disease database. MMDBM is applied to classify a large data set of numerical and categorical attributes. The data are compressed with the help of a one-dimensional wavelet transform. This is one of the new approaches applied to classified data in real-time applications.