A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification: Assessing Strengths, Weaknesses, and Future Directions

A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification: Assessing Strengths, Weaknesses, and Future Directions

Sibel Senan, Seda Keskin Tasci
Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-1062-5.ch012
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Abstract

Data mining tools are used to analyze and model data. Each of these tools has its own unique strengths and weaknesses, which make them suitable for different data mining tasks. The purpose of this chapter is to present the analysis of various data mining tools to shed light on researchers working in the field of data mining and machine learning. For this purpose, the accuracy rates of the results of different biomedical data classification applications obtained by four different data mining tools—Orange, RapidMiner, Weka, and Knime—will be evaluated. The comparisons in the context of literature research on these tools will be given. This research is particularly relevant given the increasing amount of data available in Kaggle and the need for accurate analysis and interpretation of data. By presenting the performance results of these popular data mining tools, this study will provide valuable insights for researchers and practitioners who use these tools for analysis.
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Introduction

In the era of data-driven advancements, the exponential growth of information has propelled the need for efficient and automated data mining tools to unprecedented levels. These indispensable tools play a pivotal role in extracting valuable insights and pertinent knowledge from the vast reservoirs of available data (Dwivedi, et al., 2016), (Malkawi et al., 2020). Obtaining meaningful results from data, especially with the help of machine learning (ML) algorithms, has become a popular topic in every field related to data in recent years (Allah et al., 2022), (Arasu et al., 2020), (Shobana et al., 2021). For instance, researchers in the biomedical field have conducted several studies on heart disease data using the ML approach (Devi et al., 2016), (Mohan et al. 2019), (Shobana et al., 2021), (Subramani et al., 2023), (Tougui et al., 2020).

To address this demand, this chapter proposes to conduct a comprehensive review of the existing literature, focusing on four widely utilized data mining tools: WEKA, Orange, RapidMiner, and Knime. The primary objective is to explore their applications in the realm of biomedical data classification tasks. By meticulously analyzing and comparing studies that have evaluated the performance of these tools, the chapter endeavors to illuminate their strengths and weaknesses within the context of biomedical data analysis.

The literature review will encompass a wide array of scholarly sources, including research papers, journal articles, conference proceedings, and other reputable publications. This thorough examination will provide a well-rounded understanding of the tools' performance, unveiling the nuances of their capabilities in dealing with biomedical data classification tasks. Key performance metrics, such as accuracy, precision, recall, F1-score, and computational efficiency, will be considered in the review, enabling a robust and meticulous comparison of the tools' effectiveness.

The chapter will not only highlight the unique strengths of each data mining tool but also address their inherent weaknesses, shedding light on areas where improvement or fine-tuning may be necessary. It will showcase the tools' prowess in delivering high accuracy, and effective feature selection techniques, while also acknowledging potential limitations in handling large-scale datasets, extended computation times, or challenges with certain data types. By presenting this balanced assessment, the chapter aims to provide valuable insights into the suitability of each tool for distinct data mining tasks in the biomedical domain.

Moreover, the review will identify potential gaps in the existing literature concerning the evaluation of WEKA, Orange, RapidMiner, and Knime in biomedical data classification tasks. These gaps may arise from insufficient studies on specific algorithms, limited application of the tools to particular types of biomedical data, or an inadequate consideration of crucial data preprocessing techniques. By identifying these areas of opportunity, the chapter seeks to inspire further research and exploration, fostering a deeper understanding and enhancing the effectiveness of data mining tools in the biomedical domain.

In conclusion, this chapter review will consolidate the findings from the literature, culminating in a comprehensive overview of the performance of WEKA, Orange, RapidMiner, and Knime in the context of biomedical data classification. By offering valuable insights into the tools' strengths, weaknesses, and potential avenues for improvement, this review will serve as a valuable resource for researchers, data scientists, and practitioners engaged in the dynamic and ever-expanding field of biomedical data analysis. The chapter's conclusions and recommendations will underline the importance of continuous evaluation and enhancement of data mining tools to meet the ever-evolving challenges of handling large-scale biomedical datasets. Ultimately, this chapter seeks to contribute to the ongoing progress in data mining research, empowering data-driven decision-making and advancing the frontiers of knowledge in the biomedical domain.

Key Terms in this Chapter

Machine Learning: A field of computer science related to artificial intelligence that involves algorithms that enable machines to learn from data.

Biomedical Data: Any data regarding health status that can be processed for the purpose of obtaining information.

Data Mining Tool: A framework within which data mining studies can be carried out.

Drag-and-Drop Interface: A user-friendly graphical environment for effortlessly building data analysis workflows by selecting, arranging, and connecting elements.

Data Mining: The process of searching and analyzing large amounts of raw data to identify patterns and relationships that yield valuable outputs.

Data Preprocessing: The set of processes applied to bring raw data into a form that can be implemented in data science tasks.

Ensemble Method: A group of machine learning techniques that enhance model performance by combining multiple base models' predictions to improve accuracy and reduce overfitting.

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