Demystifying the Advancements of Big Data Analytics in Medical Diagnosis: An Overview

Naik, Nithesh; Rallapalli, Yuvraj; Krishna, Manamohana; Vellara, Anoushka Suresh; Shetty, Dasharathraj K; Patil, Vathsala; Hameed, BM Zeeshan; Paul, Rahul; Prabhu, Nirmal; Prasad, Bhavan; Chłosta, Piotr; Somani, Bhaskar

doi:10.30919/es8d580

Cited by 6 publications

(3 citation statements)

References 98 publications

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“…A model with 100% false predictions has an AUC of 0.0, while a model with 100% accurate predictions has an AUC of 1.0. Statistical significance of the improvement in AUC between different methods and classifiers was calculated using standard error (SE) and a 2-tailed p -value of 0.05 [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

et al. 2023

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The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2–Class, 3–Class, and 5–Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2–class, 66% for 3–class, and 42.8% for 5–Class. The RF showed the highest accuracy of 47.6% for 5–Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model.

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Section: Methodsmentioning

confidence: 99%

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

et al. 2023

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“…Furthermore, in relation to disease surveillance, big data analytics enables predicting fatality risk by effectively examining patients' prior health data [1]. Furthermore, big data analytics has the potential to enhance disease surveillance by facilitating quicker data dissemination [6], improving the timing of surveillance information [22], [28], refining spatial and temporal resolution, expanding coverage to underserved regions, and monitoring unanticipated disease outcomes [5].…”

Section: Introductionmentioning

confidence: 99%

Ethical Framework for Multi-Source Big Data Analytics Disease Surveillance in Public Healthcare

Ramafi,

Motjolopane,

Gorejena

2023

iCARTi

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Big data analytics can revolutionize disease surveillance by integrating multiple data sources and analyzing those diverse data sources, allowing for early monitoring, detection, and response to disease outbreaks. Since the growth in technology, together with the advent of big data, there is growing interest in exploring the potential of multiple data sources for disease surveillance. However, there are still ethical concerns when it comes to big data usage for disease surveillance. Hence, this study uses of scoping review with the assistance of socio-technical systems theory, together with social contact theory to examine the ethical dimensions of employing big data analytics from diverse sources for disease surveillance, while emphasizing the importance of maintaining ethical standards in public healthcare practices. The study discovered that informed consent, privacy, and confidentiality are still major challenges when it comes to the integration of big data sources in disease surveillance. The study then recommends a unified ethical framework for enhanced disease surveillance in public healthcare through Multi-Data Source big data Analytics.

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“…An already-built pre-trained network was modified by expanding the tumor, ring-dividing it, and using T1-weighted contrast-enhanced MRI [ 34 ]. Hybridization of two methods entropy-based controlling and Multiclass Vector machine (M-SVM) is used for optimal feature extraction [ 35 , 36 , 37 ]. The differential deep-CNN model for detecting brain cancers in MRI images was put to the test by the authors in [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor Classification

et al. 2022

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Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

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Demystifying the Advancements of Big Data Analytics in Medical Diagnosis: An Overview

Cited by 6 publications

References 98 publications

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

Age Assessment through Root Lengths of Mandibular Second and Third Permanent Molars Using Machine Learning and Artificial Neural Networks

Ethical Framework for Multi-Source Big Data Analytics Disease Surveillance in Public Healthcare

A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor Classification

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