A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment

Supriya, M.; Deepa, A.J.

doi:10.1007/s10729-019-09498-w

Cited by 37 publications

(26 citation statements)

References 23 publications

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“…To help patients make the best decision in these situations, they aim to base their decision on state-of-the-art scientific knowledge [ 10 , 11 ]. Examples of recent uses of AI in this context include the detection of skin and breast cancer as well as of cardiac arrest [ 6 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Soellner

Koenigstorfer

2021

BMC Med Inform Decis Mak

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Background Advanced analytics, such as artificial intelligence (AI), increasingly gain relevance in medicine. However, patients’ responses to the involvement of AI in the care process remains largely unclear. The study aims to explore whether individuals were more likely to follow a recommendation when a physician used AI in the diagnostic process considering a highly (vs. less) severe disease compared to when the physician did not use AI or when AI fully replaced the physician. Methods Participants from the USA (n = 452) were randomly assigned to a hypothetical scenario where they imagined that they received a treatment recommendation after a skin cancer diagnosis (high vs. low severity) from a physician, a physician using AI, or an automated AI tool. They then indicated their intention to follow the recommendation. Regression analyses were used to test hypotheses. Beta coefficients (ß) describe the nature and strength of relationships between predictors and outcome variables; confidence intervals [CI] excluding zero indicate significant mediation effects. Results The total effects reveal the inferiority of automated AI (ß = .47, p = .001 vs. physician; ß = .49, p = .001 vs. physician using AI). Two pathways increase intention to follow the recommendation. When a physician performs the assessment (vs. automated AI), the perception that the physician is real and present (a concept called social presence) is high, which increases intention to follow the recommendation (ß = .22, 95% CI [.09; 0.39]). When AI performs the assessment (vs. physician only), perceived innovativeness of the method is high, which increases intention to follow the recommendation (ß = .15, 95% CI [− .28; − .04]). When physicians use AI, social presence does not decrease and perceived innovativeness increases. Conclusion Pairing AI with a physician in medical diagnosis and treatment in a hypothetical scenario using topical therapy and oral medication as treatment recommendations leads to a higher intention to follow the recommendation than AI on its own. The findings might help develop practice guidelines for cases where AI involvement benefits outweigh risks, such as using AI in pathology and radiology, to enable augmented human intelligence and inform physicians about diagnoses and treatments.

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Section: Introductionmentioning

confidence: 99%

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Soellner

Koenigstorfer

2021

BMC Med Inform Decis Mak

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“…Wuniri et al [13] proposed that feature selection is an important part to build a breast cancer classifier for the preventive diagnosis. They have suggested a method known as wrapper method that is an incorporated framework in which the feature selection datasets regarding breast cancer are implanted with the Bayesian classifiers.…”

Section: A Breast Cancermentioning

confidence: 99%

“…Multifarious intellect reasons are involved behind the causes of appearing breast cancer [11]. Breast cancer is mainly seen in 40 years old ladies [13]. A timely robust screening process in needed to diminish the mortality rate of breast cancer [12].…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

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“…AI tools such as support vector machine, PNN, other types of ML are simulated and results compared when doctors are choosing the ideal highly accurate classification method for BC detection and staging (Ayer et al, n.a. ;Suruchi, 2016;Supriya and Deepa, 2020). To get the highly accurate simulation results for BC classification in this paper, the four mentioned metaheuristic algorithms are used and the results compared, which are explained in section 4 and 5.…”

Section: Breast Cancer Predictionmentioning

confidence: 99%

Using new artificial bee colony as probabilistic neural network for breast cancer data classification

Shah

2021

FEBE

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PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

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A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment

Cited by 37 publications

References 23 publications

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Using new artificial bee colony as probabilistic neural network for breast cancer data classification

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