The ever-increasing size of datasets in the Big Data era requires effective methods for extracting meaningful information. Data Mining provides a means to analyze large datasets and uncover valuable patterns that can inform future decisions. In this study, we analyze a healthcare dataset of heart diseases to predict the likelihood of a patient having a heart disease based on specific parameters. To accomplish this, we implement decision tree classification algorithms such as ADTree, J48, and RandomForest. Additionally, a feature selection algorithm is applied to remove the least significant three attributes from the dataset, resulting in improved classification performance. Comparing the previous and current results reveals the effectiveness of this approach in enhancing the classification accuracy.
Data Mining, Classification algorithms, Feature selection
