Analysis using data mining techniques: the exploration and review data of diabetes patients / Syarifah Adilah Mohamed Yusoff ... [et al.]

Data mining is undergoing a transformative phase driven by advancements in Artificial Intelligence, statistics, database technology, real-time processing and integration of diverse data sources. These trends are not only enhancing the efficiency and accuracy of data mining but also expanding its applications across different industries. The subsequent step involves a comprehensive study of the dataset, incorporating both data exploration and analysis of data variables to achieve a structural and statistical understanding of the data. In this statistical summary procedure, the distribution of attributes and their interactions are crucial for accurately processing the data in accordance with the selected classification or data mining techniques to be performed. In examining the distribution of diabetes data, there are intricate interactions among the attributes. Therefore, it is advisable for future studies to implement robust classification algorithms, such as ensemble methods, to effectively manage and extract potential insights.