Big Data Approach for Medical Data Classification: A Review Study

Boyapati, Sumati; Swarna, Srinivasa Rao; Dutt, Vishal; Vyas, Narayan

doi:10.1109/iciss49785.2020.9315870

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…The method considers the distribution of classes surrounding the query to guarantee that the allocated weight does not negatively impact the outliers. Boyapati et al [11] concluded that the Support Vector Machine approach was better than the Decision Tree algorithm, providing a preferred dataset distribution or categorization. By accounting for the multimodal distribution of the numerical variables, Khanmohammadi and Chou's novel Gaussian Mixture Modelbased Discretization Algorithm (GMBD) maintained the most common patterns from the original dataset [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu,

Zhu

2024

ijacsa

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Data classification, a crucial practice in information management, involves categorizing data based on its sensitivity to determine appropriate access levels and protection measures. This paper explores the utilization of novel algorithms, including mouth-brooding fish (MBF), alongside machine learning techniques, for the analysis of medical health data. The SVM exhibits suboptimal performance in the task of data categorization. Therefore, Adaboost may be considered a viable substitute for MBF due to its superior performance in terms of Fscore, accuracy, specificity, and sensitivity. The accuracy of MBF, which stands at about 95%, surpasses that of Adaboost by a significant margin of 77%. The F-score, accuracy, and specificity values obtained for MBF are exceptional when compared to the other chosen models, with values of 97.17%, 93.6%, and 96.5%, respectively. The proposed algorithm exhibits promising advancements in health data categorization, offering a potential breakthrough in data classification methodologies. Leveraging this innovative approach could facilitate more accurate and efficient management of sensitive medical data, thereby enhancing healthcare systems' capabilities for data protection and analysis. The main novelty of this study lies in the introduction and evaluation of the MBF algorithm for data classification within the medical domain. Unlike traditional algorithms, MBF draws inspiration from the collective behavior of mouth-brooding fish, offering a unique optimization strategy that enhances both exploration and exploitation of the solution space. This novel approach presents a promising avenue for advancing healthcare analytics and decision-making processes.

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Section: Literature Reviewmentioning

confidence: 99%

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu,

Zhu

2024

ijacsa

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“…Volume (big), Variety, and Velocity are among the most distinguishing characteristics of Big data, and as a result, it is attractive to have an efficient classification/prediction system to learn from such Big data. Such applications include, but are not limited to, medical [15,16], financial [17,18,19], Security [20,21] and image-based applications [22,23,24,25].…”

Section: Index Termsmentioning

confidence: 99%

“…This amount of data is not restricted to social media, as many other platforms generate and store huge data volumes [4,5,6,7].This amount of data needs to be processed and analyzed in order to use it for building useful knowledge discovery and machine learning big data-based applications, like facial big data applications [8,9,10], signal big data [11] and various industry big data-based applications [12,13,14].Volume (big), Variety, and Velocity are among the most distinguishing characteristics of Big data, and as a result, it is attractive to have an efficient classification/prediction system to learn from such Big data. Such applications include, but are not limited to, medical [15,16], financial [17,18,19], Security [20,21] and image-based applications [22,23,24,25].The fundamental difficulty with big data classification is that most well-known powerful models, such as support vector machines (SVM), artificial neural networks (ANN), and decision trees, cannot be employed for big data classification…”

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confidence: 99%

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Tarawneh,

Alamri,

Al-Saedi

et al. 2023

IEEE Access

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Big data classification is a challenging task because most known classification methods need a long time and a lot of processing resources to execute such a task and use the vast amount of available data. In this paper, we propose a novel big data classification method that leverages the power of the KNN classifier and the efficiency of the ensemble learning technique to create a new method capable of performing classification tasks on big data efficiently. The proposed method picks tiny data chunks at random from a big dataset, with each chunk including random examples of a small number of randomly selected features. A weak KNN classifier is employed on each data chunk to perform classification on new (unseen) data, and the majority voting rule is used to reach the final classification decision based on the outcomes of the weak classifiers. The proposed method has a constant classification time, according to the time complexity analysis. Furthermore, the proposed method was found to be more efficient on a single node than existing methods, some of which run on a large cluster of nodes. Because of its speed and enhanced performance, the proposed method can be considered an ideal classifier for handling complex data types such as Geospatial data, Big trajectory data, and Big Data in general. INDEX TERMSBig data; Geospatial data; Trajectory data; Classification; Ensemble learning; KNN I. INTRODUCTIONFor decades, researchers have been studying and evaluating big data methodologies and tools. This interest stems from the massive volume of data exchanged and saved by social media users, medical organizations, educational institutions, and others.According to statistical reports, the number of users on different social media platforms has reached more than 2 billion [1]. WhatsApp, for example, has over 600 million users, more than half a billion photos, and one hundred million videos transferred and shared between users on a daily basis.[2]. Also, due to the huge advance in smartphone technology, it has become easier for users to share text\images and write posts on such social media platforms. Some reports show that the number of posts on Twitter in 2007 was 5K, this number became around 500 million after about 6 years, in 2013 [3], which indicates the massive amount of available data on social media in general. This amount of data is not restricted to social media, as many other platforms generate and store huge data volumes [4,5,6,7].This amount of data needs to be processed and analyzed in order to use it for building useful knowledge discovery and machine learning big data-based applications, like facial big data applications [8,9,10], signal big data [11] and various industry big data-based applications [12,13,14].Volume (big), Variety, and Velocity are among the most distinguishing characteristics of Big data, and as a result, it is attractive to have an efficient classification/prediction system to learn from such Big data. Such applications include, but are not limited to, medical [15,16], financial [17,18,19]...

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“…Big medical data and image detection is an essential element of healthcare that plays a critical role in the storage, organization, and analysis of medical information [ 4 ]. the effective classification of medical data enables the efficient retrieval and examination of patient records, which can aid in the diagnosis and treatment of illnesses.…”

Section: Introductionmentioning

confidence: 99%

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

Awad

Hamad

Alzubaidi

2023

Life

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Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.

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Big Data Approach for Medical Data Classification: A Review Study

Cited by 13 publications

References 23 publications

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

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