Cancer Prediction Based on Fuzzy Inference System

Dutta, Soumi; Ghatak, Sujata; Sarkar, Abhijit; Pal, Rechik; Pal, Rohit; Roy, Rohit

doi:10.1007/978-981-13-2414-7_13

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…In an investigation by [17] a fuzzy model is presented in order for evaluating the diagnosis of the breast cancer, one of the characteristics of which is displaying the potential relationship between the variables of age and size of the tumor. This system helps the breast cancer of the patient to be detected as soon as possible.…”

Section: Review Of Previous Methodsmentioning

confidence: 99%

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019

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Breast cancer is the second leading cancer among women worldwide, the incidence of which has been reported to increases every year due to factors such as inheritance, lifestyle, and dietary habits [1]. Industrialization and urbanization are among the other contributing factors to the increase incidence of breast cancer. On the other hand, new facilities have been used for the early diagnosis of this chronic disease. The prevalence rate of breast cancer has been reported to be higher in high-income countries, while the incidence rate is on the rise in low-income countries as well, such as the regions in Africa, Asia, and Latin America [2]. In India, the mean age range of the high-risk population for breast cancer has been estimated at 43-46 years, and women aged 53-57 years are reported to be more susceptible to breast cancer [3].

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Section: Review Of Previous Methodsmentioning

confidence: 99%

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

2019

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“…However, further improvement is needed for the classification of trained dataset. Dutta et al [52] evaluated the breast cancer. The fuzzy logic with inference system is used to distinguish breast lumps with the tumour.…”

Section: Classification Technique To Predict Various Stages Of Cancermentioning

confidence: 99%

“…Several usability studies have been summarised in Table 2 based on their intended use. [52], [54] [55], [56] [57]- [59] [60]- [62] [63], [64] [65], [66] [67], [68] [69]- [72] [73]- [75] [76]- [78] [79], [80] [81], [82] [83], [84] To keep up with the tremendous growth in biomedical and healthcare papers, we've undertaken a comprehensive assessment of healthcare analytics across five subfields as illustrated in Table 3. Big data analytics is being used in the field of bioinformatics because to its complicated and enormous datasets, according to the results of the study.…”

Section: Big Data In Healthcarementioning

confidence: 99%

Complex predictive analysis for health care: a comprehensive review

2023

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Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.

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“…Fuzzy inference-based classification system was used to find the growth of tumor, whether it converts or not into breast cancer, by Dutta et al [25]. The prediction is based on the behavior of tumor genetics.…”

Section: Related Workmentioning

confidence: 99%

Stacking-Based Ensemble Framework and Feature Selection Technique for the Detection of Breast Cancer

Chaurasia

Pal

2021

SN COMPUT. SCI.

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Breast cancer is the second most common cancer in women worldwide. The uncontrolled growth of breast cells is called breast cancer. The treatment of human breast cancer is a very critical process, and sometimes certain indicators may produce negative results. To avoid this misleading outcome situation, a reliable and accurate breast cancer diagnosis system must be available. The machine learning (ML) method is a modern and accurate technique that researchers have recently applied to predict and diagnose breast cancer. In this research article, we developed stack-based ensemble techniques and feature selection methods for the comprehensive performance of the algorithm and comparative analysis of breast cancer datasets with reduced attributes and all attributes. In this article, we first take the SVM, k nearest neighbors, Naive Bayes and perceptron as four ML algorithms as sub-models that have been trained and predicted from, and then combine them to make a new model called blending (stacking). Finally, logistic regression is used to predict the stacked model. It is significant that sub-models produce different results that are not correlated predictions. The stacking technique is best when all the submodels are skillfully combined together. This article uses the five-feature selection technique because it affects the overall performance of the model. Unrelated or moderately related features may adversely affect the behavior of the model. After applying the feature selection method, now we have data set with reduced features as well as all features. We implemented logistic regression on a dataset with all features and a dataset with reduced features. Finally, we see that the dataset with reduced features has got improved accuracy.

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Cancer Prediction Based on Fuzzy Inference System

Cited by 9 publications

References 2 publications

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

Complex predictive analysis for health care: a comprehensive review

Stacking-Based Ensemble Framework and Feature Selection Technique for the Detection of Breast Cancer

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