An Expanded Vision of Breast Cancer Diagnosis Approaches Based on Machine Learning Techniques

Abdullah, Alaa; Hasan, Taha Mohammed; Waleed, Jumana

doi:10.1109/iec47844.2019.8950530

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Kanker payudara adalah suatu penyakit yang mana terjadi pertumbuhan atau perkembangan tidak terkontrol dari sel-sel (jaringan) payudara (Abdullah, Hasan, & Waleed, 2019), dan kondisi ini bisa saja terjadi pada wanita maupun pria. Kanker payudara merupakan gangguan payudara yang sangat ditakuti wanita.…”

Section: Abstrakunclassified

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Media¹

2022

JPN

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Cancer in West Sumatra is a problem that needs serious attention. The purpose of this article is to describe the treatment-seeking behavior in breast cancer patients and the factors behind the treatment-seeking behavior. The research method used is a combination of qualitative and quantitative descriptive methods. Data collection techniques were carried out through direct interviews with respondents, in-depth interviews with informants, and observation. The study's results revealed that the treatment-seeking behavior that breast cancer patients mostly carried out was medical treatment and traditional medicine (70%). Most of the respondents (77%) had already run some type of medical treatment (surgery, and chemotherapy). The reason some respondents did not take medical treatment was that their family did not allow it (43%), limited funds (29%), and fear (28%). The factors behind seeking treatment behavior are knowledge/perceptions and beliefs of the community, economic conditions and access to health services, and environmental and psychological factors. The recommendations proposed include optimizing socialization and education about breast cancer disease and control to the community, increasing community participation in early detection of breast cancer, increasing public awareness and participation in breast cancer screening and treatment by utilizing health workers in health facilities.

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Section: Abstrakunclassified

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Media¹

2022

JPN

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“…To address these challenges, recent research developments are collecting social users' posts in news articles to help deduce fake articles, giving some promising early results [3]. Generally, any machine learning-based detection scheme involves diverse phases [4], as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Effective Detection Scheme of Fake News

Kamel,

Waleed

2022

J. Al-Qadisiyah Comp. Sci. Math.

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Today, extremely large amounts of false news are consistently uploaded by malevolent people with fraudulent intentions, endangering democracy, justice, and public confidence while having highly harmful social impacts on both individuals and society. Due to the inherent uncontrollable posting processes of social media sites (such as Facebook, Twitter, and Snapchat), this is particularly pertinent to them. The need for earlier false news identification has considerably fueled efforts in academia and business to create more accurate fake news detection techniques. Unfortunately, there isn't much information available regarding how news spreads. There are benefits and drawbacks to relying on social media to follow the news. Social media platforms do make it possible for information to flow swiftly among users. However, these websites could be used to disseminate "fake news," which is low-quality content that contains errors. The widespread dissemination of false information has an extremely damaging effect on both people and society. As a result, the detection of fake news posted on numerous social media platforms has recently emerged as a highly regarded field of study. In this paper, an effective detection scheme of fake news based on the commonly utilized techniques of machine learning has been presented. This proposed scheme involves diverse phases; Dataset preprocessing phase, extracting the phase of the feature, and Naive Bayes (NB) and K-nearest Neighbor are used in the categorization step (KNN). The obtained results in this presented scheme exhibit that the utilization of the NB classification technique exceeds the K-NN classification technique with an accuracy of 94% using the same dataset.

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“…The techniques of machine learning are widely employed to detect and classify various kinds of cancers since they provide accurate and high-performance results. These techniques can be vastly utilized for disease diagnosis in the medical field [3]. Therefore, the studies they are devoted to are of scientific relevance.…”

Section: Introductionmentioning

confidence: 99%