Predicting onset (type-2) of diabetes from medical records using binary class classification

Mamun, Md Habib Al; Keikhosrokiani, Pantea

doi:10.1016/b978-0-323-91907-4.00012-1

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…In this method, a key assumption is that there's no interdependence among the feature pairs, resulting in two pivotal presumptions: feature independence and attribute equality. Naive Bayes classifiers are versatile, existing in three primary variants: Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes 33 . The choice of variant depends on the nature of the data being analyzed.…”

Section: Methodsmentioning

confidence: 99%

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Mathivanan,

Francis,

Srinivasan

et al. 2024

Sci Rep

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Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

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Section: Methodsmentioning

confidence: 99%

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Mathivanan,

Francis,

Srinivasan

et al. 2024

Sci Rep

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Comparison of Different Binary Classification Algorithms for Malware Detection

Barzev,

Borissova,

Buhtiyarov

2024

Lecture Notes in Networks and Systems

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From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

Mukherjee,

Abraham,

Singh

et al. 2024

Mol Biotechnol

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In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

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Predicting onset (type-2) of diabetes from medical records using binary class classification

Cited by 5 publications

References 8 publications

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Comparison of Different Binary Classification Algorithms for Malware Detection

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

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