A clinical decision support system for breast cancer patients

JOURNAL OF ASSOCIATED MEDICAL SCIENCES (Online), 2024

Background: Many studies employed machine learning (ML) to forecast the prognosis of breast cancer (BC) patients and discovered that the ML model showed high individualized forecasting ability. Breast cancer is the most frequent kind of carcinoma in women globally and ranks as the leading cause of death in women. Objectives: This study intends to use the Surveillance, Epidemiology, and End Results dataset to categorize breast carcinoma cases' alive and dead conditions. Deep learning and machine learning have been extensively utilized in clinical studies to address various categorization problems due to their ability to manage massive data sets in an organized manner. Pre-processing the data allows it to be visualized and analyzed for making critical choices. This study describes a realistic machine learning-based strategy for categorizing the SEER breast cancer dataset. Materials and methods: We employed classification and machine learning algorithms to classify breast cancer mortality. Four well-known classification ML algorithms were employed in this study. To identify risk factors, we employed multivariate analysis using the data set. Results: The decision tree performed the best accuracy (0.914) among all the models. T4 stage (β=1.4, p<0.001, OR=4.22, 95% CI (2.06-8.64), N2 stage (β=0.39, p=0.008, OR= 1.49, 95% CI (1.111-1.997) found to be major risk factors for breast cancer mortality using multivariate analysis. Conclusion: The significant prognostic variables affecting the breast carcinoma survival rates reported in the current research are relevant and might be turned into decision support systems in the medical realm.

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007

A three stage development process for the production of a hierarchical rule based prognosis tool is described. The application for this tool is specific to breast cancer patients that have a positive expression of the HER 2 gene. The first stage is the development of a Bayesian classification neural network to classify for cancer specific mortality. Secondly, low-order Boolean rules are extracted form this model using an Orthogonal Search based Rule Extraction (OSRE) algorithm. Further to these rules additional information is gathered from the Kaplan-Meier survival estimates of the population, stratified by the categorizations of the input variables. Finally, expert knowledge is used to further simplify the rules and to rank them hierarchically in the form of a decision tree. The resulting decision tree groups all observations into specific categories by clinical profile and by event rate. The practical clinical value of this decision support tool will in future be tested by external validation with additional data from other clinical centres.

Breast cancer survival prediction is an important step in the complex decision process. The present study investigates the effects of prognostic variables on the risk of breast cancer failure/survival using feed forward neural network. The neural network was trained and tested using six hundred and fifty breast cancer patients. 15 prognostic variables were used in this study as features for the input vectors. Multivariate analysis on survival was performed using a recursive partitioning namely classification and regression trees (CART). The results show that the accuracy of artificial neural network (ANN) for the survival prediction was better than regression based approaches.

Artificial Intelligence in Medicine, 2005

Objective: The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Therefore, the main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability.

2017

Abstract— Breast cancer is one of the deadliest disease, is the most common of all cancers and is the leading cause of cancer deaths in women worldwide, accounting for &gt;1.6% of deaths and case fatality rates are highest in low-resource countries. The breast cancer risks are broadly classified into modifiable and non –modifiable factors. The non modifiable risk factors are age, gender, number of first degree relatives suffering from breast cancer, menstrual history, age at menarche and age at menopause. While the modifiable risk factors are BMI, age at first child birth, number of children, duration of breast feeding, alcohol, diet and number of abortions. This paper presents a diagnosis system for detecting breast cancer based on RepTree, RBF Network and Simple Logistic. In test stage, 10-fold cross validation method was applied to the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia database to evaluate the proposed system performances. The correct classif...

Breast cancer is one of the deadliest disease, is the most common of all cancers and is the leading cause of cancer deaths in women worldwide, accounting for >1.6% of deaths and case fatality rates are highest in low-resource countries. The breast cancer risks are broadly classified into modifiable and non – modifiable factors. The non-modifiable risk factors are age, gender, number of first degree relatives suffering from breast cancer, menstrual history, age at menarche and age at menopause. While the modifiable risk factors are BMI, age at first child birth, number of children, duration of breast feeding, alcohol, diet and number of abortions. This paper presents a diagnosis system for detecting breast cancer based on RepTree, RBF Network and Simple Logistic. In test stage, 10-fold cross validation method was applied to the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia database to evaluate the proposed system performances. The correct classification rate of proposed system is 74.5%. This research demonstrated that the Simple Logistic can be used for reducing the dimension of feature space and proposed Rep Tree and RBF Network model can be used to obtain fast automatic diagnostic systems for other diseases. Keywor

2019

The new approach in cancer research that shifted from pure long-term biological and clinical experiments to computer-generated experiments is the main inspiration for this project. Three Data mining techniques (Decision Trees, Neural Networks and Naive Bayes) were built to compare their performance based on three main parameters: accuracy, sensitivity and specificity. The experiment was set up with multi-layer perceptron as the baseline scheme and with statistical significance of 0.05. The models were built using data collected from Saudi Arabia, more specifically, from King Faisal Specialist Hospital and Research Center. The prediction is based on 8 attributes: age, birth location, reason for no radiation, laterality, grade, sex, primary site and marital status. However, the data collected had around 680 instances which were not sufficient to build the models. Sampling with a random seed was completed to double the size of the training dataset. The results showed that decision tree...

Expert Systems with Applications, 2009

Decision tree C&RT CHAID QUEST ID3 C4.5 C5.0 Cox regression Kaplan-Meier Breast cancer Disease-free survival Random survival forests a b s t r a c t

This study concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods: two from machine learning (artificial neural network and decision trees) and one from statistics (logistic regression), and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 87 variables and the total of the records are 457,389; which became 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we find the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2 which is in our view of point is the suitable system to use according to the facilities and the results given to us. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. However, we have found out that the neural network has a much better performance than the other two techniques. Finally, we can say that the model we chose has the highest accuracy which specialists in the breast cancer field can use and depend on.

SN Computer Science, 2021

Nowadays, people are facing various health-related problems due to the modern life style what they follow. Breast Cancer is one of the most common problems among women worldwide which affects approximately 2.1 million women each year. Hence, it has become paramount to develop a system that can identify the major risk factors of Breast Cancer beforehand to make women aware about the risk factors and to take some precautionary measures to manage Breast Cancer. Consequently, this paper proposes a system called Transparent Breast Cancer Management System using P-Rules (TBCMS-PR) which identifies the major risk factors responsible for Breast Cancer in detail using decision tree and neural Network. TBCMS-PR uses decision tree to generate the rules for deciding the decision of Breast Cancer. Neural Network is used to keep only the relevant rules for Breast Cancer and to drop the irrelevant ones. Finally, the major risk factors with ranges are identified based on Sequential Search algorithm. The performance of the TBCMS-PR system is validated with the Breast Cancer dataset collected from UCI repository and is compared with a recent existing system. From the experimental results, it is observed that the proposed TBCMS-PR is significant and potential to manage Breast Cancer to a great extent by managing only one or two major risk factor(s).