scholarly journals Application of Artificial Intelligence in the prediction of breast cancer survival in Mexican women

2020 ◽  
pp. 6-13
Author(s):  
Julia Patricia Melo-Morin ◽  
María de los Ángeles Ahumada-Cervantes ◽  
Gil Santana-Esparza
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Data Mining ◽  
Cancer Survival ◽  
Malignant Tumors ◽  
Data Mining Techniques ◽  
Tertiary Hospitals ◽  
General Survival ◽  
Cancer Specialists ◽  
Modeling Data

In Mexico, the leading cause of death caused by malignant tumors in women is breast cancer and the general survival of five years treated in facilities of the Public Health System is between 75 and 80%. There are applications that determine the survival of patients with breast cancer, based on the use of drugs that are not prescribed in Mexico, so cancer specialists cannot consider the information offered by these programs for decision-making with patients Mexican. This article describes the development of an expert system that, by applying artificial intelligence techniques, allows the evaluation and prediction of patient survival, based on a model generated with data mining techniques. Rules were obtained from the patterns obtained with data collected from patients with breast cancer since 2006. The development of the system is governed by the Knowledge Discovery from Databases (KDD) methodology, supported by the WEKA tool for modeling data mining techniques. There is a data warehouse of 4,773 women with breast cancer provided by two tertiary hospitals in Mexico City: an INCan cohort of 4,300 patients diagnosed from 2006 to 2013 with a median follow-up of 40.5 months of survival and an INCMSZprovided cohort of 473 patients from 2011 to 2018 with a median of 39 months.

Factors influencing HER2 positivity in breast cancer according to statistical modeling and data mining techniques based on a real-world national database: HER-France.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. e12086-e12086
Author(s):  
Marie-Pierre Chenard ◽  
Eric Anger ◽  
Marie-Helene Bizollon ◽  
Jerome Chetritt ◽  
Francesco Bruno Cutuli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Statistical Modeling ◽  
Real World ◽  
National Database ◽  
Data Mining Techniques ◽  
Factors Influencing ◽  
Her2 Positivity

scholarly journals Prediction and Classification into Benign and Malignant using the Clinical Testing Features

2020 ◽  
Vol 9 (10) ◽  
pp. 55-61
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Image Classification ◽  
Naive Bayes ◽  
Malignant Tumors ◽  
Naïve Bayes ◽  
Support Vector ◽  
Natural Image ◽  
Data Set ◽  
Classification Techniques

Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction.

scholarly journals Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis

2020 ◽  
pp. 584-596
Author(s):  
Morales-Ortega Roberto Cesar ◽  
Lozano-Bernal German ◽  
Ariza-Colpas Paola Patricia ◽  
Arrieta-Rodriguez Eugenia ◽  
Ospino-Mendoza Elisa Clementina ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Recurrence Analysis ◽  
Data Mining Techniques

Artificial Intelligence Applications in Tourism

2011 ◽  
pp. 241-247 ◽  
Author(s):  
Carey Goh ◽  
Henry M.K. Mok ◽  
Rob Law
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Investment Planning ◽  
Data Mining Techniques ◽  
Travel And Tourism ◽  
The Future ◽  
Multiplier Effects

The tourism industry has become one of the fastest growing industries in the world, with international tourism flows in year 2006 more than doubled since 1980. In terms of direct economic benefits, United Nations World Tourism Organization (UNWTO, 2007) estimated that the industry has generated US $735 billion through tourism in the year of 2006. Through multiplier effects, World Travel and Tourism Council (WTTC, 2007) estimated that tourism will generate economic activities worth of approximately US $5,390 billion in year 2007 (10.4% of world GDP). Owing to the important economic contribution by the tourism industry, researchers, policy makers, planners, and industrial practitioners have been trying to analyze and forecast tourism demand. The perishable nature of tourism products and services, the information-intensive nature of the tourism industry, and the long lead-time investment planning of equipment and infrastructures all render accurate forecasting of tourism demand necessary (Law, Mok, & Goh, 2007). Past studies have predominantly applied the well-developed econometric techniques to measure and predict the future market performance in terms of the number of tourist arrivals in a specific destination. In this chapter, we aim to present an overview of studies that have adopted artificial intelligence (AI) data-mining techniques in studying tourism demand forecasting. Our objective is to review and trace the evolution of such techniques employed in tourism demand studies since 1999, and based on our observations from the review, a discussion on the future direction of tourism research techniques and methods is then provided. Although the adoption of data mining techniques in tourism demand forecasting is still at its infancy stage, from the review, we identify certain research gaps, draw certain key observations, and discuss possible future research directions.

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