Using Machine Learning algorithms for breast cancer risk prediction and diagnosis

Bharat, A.V.L.P; Pooja, N; Reddy, Rasika

doi:10.1109/cimca.2018.8739696

Cited by 89 publications

(33 citation statements)

References 1 publication

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“…The root-mean-squared error is being used to evaluate regression predictions, while accuracy is being used to evaluate classification predictions. [ 5 ]. Supervised learning has the goal of predicting a known output based on a common dataset.…”

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

“…Euclidean distance is the most popular approach used to calculate the distance. The training dataset should be vectors in a multidimensional feature space, each with a class label [ 5 ].…”

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

“…Then, it identifies the hyperplane that separates the datapoints into two classes. By this, the marginal distance between the decision hyperplane and instances that are close to the boundary can be maximized [ 5 ].What brings SVM ahead of other algorithms is that it has basic functions that can map points to other dimensions by using nonlinear relationships [ 8 ]. As it divides the datapoints to two classes, SVM is also known as the nonprobabilistic binary classifier.…”

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

“…The representation for the CART model is a binary tree in which each root represents a single input and a split point on that variable. Leaf nodes contain an output which is used to make predictions [ 5 ].…”

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

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Involvement of Machine Learning Tools in Healthcare Decision Making

Jayatilake

Ganegoda

2021

Journal of Healthcare Engineering

165

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In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

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Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

“…Euclidean distance is the most popular approach used to calculate the distance. The training dataset should be vectors in a multidimensional feature space, each with a class label [ 5 ].…”

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

Section: Machine Learning Approaches and Algorithmsmentioning

confidence: 99%

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Involvement of Machine Learning Tools in Healthcare Decision Making

Jayatilake

Ganegoda

2021

Journal of Healthcare Engineering

165

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“…Seigel R L et.al [1] gives particulars about cancer for clinicians research on Wisconson dataset [2]. Anusha Bharat et al [8]presented Machine Learning Algorithms like KNN, Logistic Regression, Niave Bayes and Decision trees by performing Analysis Using Wisconsin Breast cancer Dataset [9]. Performance of each algorithm varies depending on parameter selection.…”

Section: Review Of Literaturementioning

confidence: 99%

Machine Learning Approaches for Healthcare Data Analysis

Eedi¹,

Kolla²

2020

jcr

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Breast cancer is the most common cancer in women worldwide and it remains the most common cause of cancer-related death in woman globally. Machine Learning techniques have been proven to be of great help in prognosis and diagnosis of various health related issues. This work constitutes a comparison of five machine learning (ML) algorithms: Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Decision Tree (DT), Random Forest (RF) on the Breast Cancer Wisconsin Diagnostic (BCWD) dataset. Features were extracted from the digitized images of FNA tests on a breast mass. Results show that Random Forests performs better among all the models across different classification metrics such as accuracy, precision, recall, and f1-score.

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