A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data Context Article Swipe
Madhu Kirola
,
Minakshi Memoria
,
Ankur Dumka
,
Amrendra Tripathi
,
Kapil Joshi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.13005/bpj/2339
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.13005/bpj/2339
In recent years, big data in health care is commonly used for the prediction of diseases. The most common cancer is breast cancer infections of metropolitan Indian women as well as in women worldwide with a broadly factor occurrence among nations and regions. According to WHO, among 14% of all cancer tumours in women breast cancer is well-known cancer in women in India also. Few researches have been done on breast cancer prediction on Big data. Big data is now triggering a revolution in healthcare, resulting in better and more optimized outcomes. Rapid technological advancements have increased data generation; EHR (Electronic Health Record) systems produce a massive amount of patient-level data. In the healthcare industry, applications of big data will help to improve outcomes. However, the traditional prediction models have less efficiency in terms of accuracy and error rate. This review article is about the comparative assessment of complex data mining, machine learning, deep learning models used for identifying breast cancer because accuracy rate of any particular algorithm depends on various factors such as implementation framework, datasets(small or large),types of dataset used(attribute based or image based)etc. Aim of this review article is to help to choose the appropriate breast cancer prediction techniques specifically in the Big data environment to produce effective and efficient result, Because “Early detection is the key to prevention-in case of any cancer”.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.13005/bpj/2339
- OA Status
- diamond
- Cited By
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- References
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4226140354Canonical identifier for this work in OpenAlex
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https://doi.org/10.13005/bpj/2339Digital Object Identifier
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A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data ContextWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
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2022-03-31Full publication date if available
- Authors
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Madhu Kirola, Minakshi Memoria, Ankur Dumka, Amrendra Tripathi, Kapil JoshiList of authors in order
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https://doi.org/10.13005/bpj/2339Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.13005/bpj/2339Direct OA link when available
- Concepts
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Big data, Breast cancer, Context (archaeology), Computer science, Machine learning, Artificial intelligence, Deep learning, Health care, Predictive modelling, Cancer, Data mining, Data science, Medicine, Internal medicine, Economic growth, Biology, Economics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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31Total citation count in OpenAlex
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2025: 8, 2024: 8, 2023: 9, 2022: 6Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3083171672, https://openalex.org/W2806359329, https://openalex.org/W2370924594, https://openalex.org/W2953605441, https://openalex.org/W2989134510, https://openalex.org/W2988718426, https://openalex.org/W4240433105, https://openalex.org/W2982822400, https://openalex.org/W2791030877, https://openalex.org/W2951404122, https://openalex.org/W2806419994, https://openalex.org/W2981089454, https://openalex.org/W2936214295, https://openalex.org/W4243162126, https://openalex.org/W2968058863, https://openalex.org/W3108808737, https://openalex.org/W2908849530, https://openalex.org/W2942203109, https://openalex.org/W2972736028, https://openalex.org/W2042025453, https://openalex.org/W3021329907, https://openalex.org/W2992252600, https://openalex.org/W3217619566, https://openalex.org/W2940689983, https://openalex.org/W2604686853, https://openalex.org/W2945819472, https://openalex.org/W2944939776, https://openalex.org/W2906221808, https://openalex.org/W2914383167, https://openalex.org/W3104768730, https://openalex.org/W2746217632, https://openalex.org/W3013538309, https://openalex.org/W2889646458, https://openalex.org/W2982663567, https://openalex.org/W2964562366, https://openalex.org/W2989272346, https://openalex.org/W2907031566, https://openalex.org/W2952523812, https://openalex.org/W2952493603, https://openalex.org/W2899432087, https://openalex.org/W2973569693, https://openalex.org/W2965231525, https://openalex.org/W2914899668, https://openalex.org/W2907066882, https://openalex.org/W3113014766, https://openalex.org/W2979414334, https://openalex.org/W2621586148, https://openalex.org/W2993303538 |
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