A Comparative Study on Data Mining Approach Using Machine Learning Techniques: Prediction Perspective

Mishra, Anshul; Khan, Mohd Hammad; Khan, Waris; Khan, Mohammad Zunnun; Srivastava, Nikhil Kumar

doi:10.1007/978-3-030-77746-3_11

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Moreover, in [33], the authors discussed various ML algorithms, such as naïve Bayes, classification and regression tree (CART), decision tree (DT), and support vector machine (SVM), for different diseases, such as lung cancer, breast cancer, and skin diseases. In [34], the authors present a comparative study on data mining techniques for breast cancer prediction, including naïve Bayes, back-propagated neural networks, and decision tree algorithms. In [35], the authors investigated the effectiveness of numerous nature-inspired computing techniques, such as genetic algorithms [36], ant colony optimization [37], particle swarm optimization [38], and artificial bee colony [39], for diagnosing various critical human disorders.…”

Section: Background and Related Workmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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Section: Background and Related Workmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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“…• AI has been shown to increase clinical efficiency and accuracy, increase the speed of diagnosis, improve patient outcomes, and speed up patient recovery through the use of precision medicine and robotic surgery [47].…”

Section: Examples Of Cutting-edge Technologiesmentioning

confidence: 99%

“…In order to achieve their aims and objectives, data-driven companies are concentrating on gathering as much timely information as possible. Data-driven businesses expand rapidly as a result of strategies centered on data analysis and utilization [3]. On the other hand, data-driven businesses face challenges like privacy leaks, data silos, inefficient data administration, a lack of data skills, data that is inaccessible, and difficulties locating the appropriate cutting-edge technology.…”

Section: Introduction-mentioning

confidence: 99%

The Cutting Edge Technologies in Computer Science: A Review

Abdullah¹

2023

ijarcs

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Technological gadgets, methods, and accomplishments that make use of the most recent and advanced IT advances are examples of cutting-edge technology. "Cutting edge" is a term commonly used to describe the most advanced and forward-thinking companies in the IT industry. The term "cutting-edge technology" is used to describe the most advanced and up-to-date technological features, as opposed to "bleeding-edge technology," which is so novel that it presents risks to consumers. While the term "technology" is most often associated with computer and electronic devices, it can refer to advancements in virtually any field. The author of this paper has properly cited the most up-to-date cutting-edge research.

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“…Only 5% of the previous data's [7] Accuracy, Specificity, and Significance levels remained after the multi pre-processing, indicating that the proposed approach outperformed the other algorithms. A comparison of data mining classification algorithms is therefore presented [8], which focuses on data mining using the learning approach. There are a lot of options in this field when it comes to imaging modalities.…”

Section: Related Workmentioning

confidence: 99%

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

Ghantasala

Raparthi

B³

et al. 2023

Preprint

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Breast cancer prediction is an important topic in the field of healthcare. Breast cancer is one of the most common cancers in women and early detection is critical for successful treatment. There are several methods for predicting breast cancer, including imaging studies, genetic testing, and risk assessment models. Early detection can greatly improve the chances of successful treatment and long-term survival. One approach to detecting breast cancer is to use machine learning algorithms such as support vector machine (SVM) classifiers. SVMs are a popular type of supervised learning algorithm that can be used for classification or regression analysis. To use SVMs for breast cancer classification, you need to first prepare the data by dividing it into training and testing sets. The training set is used to train the SVM model, and the testing set is used to evaluate the performance of the model. The SVM model learns to classify the data by adjusting the parameters of the kernel function. In this paper, the performance of Linear, Polynomial, Gaussian and Sigmoid machine-learning kernels in the Support Vector Machine method was investigated to determine which kernel classifier is better at diagnosing breast cancer. In addition, this study made usage of the Wisconsin Breast Cancer (Diagnostic) dataset that contains 569 occurrences and 32 features for analysis. The major objective of this study is to compare a variety of kernel classifiers to identify the one that provides the best accuracy. Linear kernel support vector machine was shown to have the highest accuracy (97.90%) and lowest false discovery rates in this investigation. In contrast, other kernels and classification algorithms show low performance, which may not be more accurate in breast cancer prediction.

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A Comparative Study on Data Mining Approach Using Machine Learning Techniques: Prediction Perspective

Cited by 5 publications

References 17 publications

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

The Cutting Edge Technologies in Computer Science: A Review

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

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