19,510 research outputs found
A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications
In the developing world, cancer death is one of the major problems for humankind.
Even though there are many ways to prevent it before happening, some cancer types still do not have
any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the
most important thing in its treatment. Accurate diagnosis is one of the most important processes
in breast cancer treatment. In the literature, there are many studies about predicting the type of
breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg
of the University of Wisconsin Hospital were used for making predictions on breast tumor types.
Data visualization and machine learning techniques including logistic regression, k-nearest neighbors,
support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to
this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques
and visualization. The paper aimed to make a comparative analysis using data visualization and
machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of
applications were comparable for detecting breast cancers. Data visualization and machine learning
techniques can provide significant benefits and impact cancer detection in the decision-making
process. In this paper, different machine learning and data mining techniques for the detection of
breast cancer were proposed. Results obtained with the logistic regression model with all features
included showed the highest classification accuracy (98.1%), and the proposed approach revealed
the enhancement in accuracy performances. These results indicated the potential to open new
opportunities in the detection of breast cancer.No sponso
BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer
For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice
A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer
Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice
Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer
Breast cancer is one of the leading causes of death in females and survival
depends on early diagnosis and treatment. This paper applied machine
learning techniques in prediction of breast cancer survival (dead or alive) using
age, sex, length of stay, mode of diagnosis and location of cancer as
predictors (independent variables). The data was obtained from the outpatient
department of the University of Ilorin Teaching Hospital, Ilorin, Nigeria. The
sample size of 300 consists of 175 females and 25 males who were admitted at
the hospital and treated for breast cancer. The patients were later discharged
or died. Adaptive boosting (AdaBoost) performed best out of the data mining
models used in the classification in all the three cases where the target class is
average over classes, alive or dead. The AdaBoost performed best with the
classification accuracy and area under curve (AUC) of 98.3% and 99.9%
respectively. Furthermore, a probe on the prediction by AdaBoost showed that the probability of dead due to breast cancer is 0.47, which the length of stay
hugely contributed to the high probability, location of breast cancer and
mode of diagnosis contributed minimally while age and sex contributed
insignificantly. The high probability of breast cancer mortality predicted in this
paper is a call for concern as early detection of breast cancer, routine breast
examination and breast cancer awareness are crucial in increasing the
probability of survival. The results can be used to design a decision support
system that can increase the chances of breast cancer survival
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Disease modelling using evolved discriminate function
Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model
Passively mode-locked laser using an entirely centred erbium-doped fiber
This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train
Disease modeling using Evolved Discriminate Function
Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model
An Intelligent Data Mining System to Detect Health Care Fraud
The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice
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