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ISBN: 978-0-7354-4318-1
PRELIMINARY
THE 3RD FACULTY OF INDUSTRIAL TECHNOLOGY INTERNATIONAL
KEYNOTE SPEAKERS
MECHANICAL ENGINEERING/OIL &
GAS/MANUFACTURING OPERATIONS OF PERFORMANCE
ELECTRICAL ENGINEERING/POWER
CHEMICAL ENGINEERING
Ramizah Kamaludin, Husnul Azan Tajarudin, Charles Wai Chun Ng, Azieyati Hani Hussain, Shakera Jakera en Mahmod Abobaker. Shakera Jakera, Husnul Azan Tajarudin, Charles Wai Chun Ng, Ramizah Kamaludin, Azieyati Hani Hussain en Mahmod Abobaker. Azieyati Hani Hussain, Husnul Azan Tajarudin, Charles Wai Chun Ng, Ramizah Kamaludin, Shakera Jakera en Mahmod Abobaker.
INFORMATICS/INFORMATION SYSTEMS
INDUSTRIAL ENGINEERING
The application of sweep algorithm and nearest neighbor algorithm to solve the heterogeneous fleet routing problem of subsidized gas distribution. Storing matrix and nearest neighborhood permutation for vehicle routing problem using Google Maps and VBA. Application of interpretive structural modeling (ISM) and analytical network process methods (ANP) in the selection of suppliers of convenient wheel components in bicycle.
Improved business productivity based on supply chain management performance measurement using the Objective Matrix (OMAX) method.
Analysis of logistic regression algorithm for predicting types of breast cancer based on
Analysis of a logistic regression algorithm for predicting breast cancer types based on machine learning.
Analysis of Logistic Regression Algorithm for Predicting Types of Breast Cancer Based on Machine Learning
INTRODUCTION
In this study, predictions of the type of breast cancer will be performed using a different algorithm with different evaluation methods and calculating error values from previous studies with an accuracy value of 96.50%. Based on Figure 2 above, the correlation shows how related changes are between the two attributes in the data set. If two attributes change in the same direction, the two attributes are positively correlated.
High-quality data, namely data that does not contain noise and data that does not have incorrect data in the dataset, a good classification accuracy depends on the quality of the data that has been processed and achieved through the pre-processing stage [19] . The training data will be used to train the algorithm to produce a trained classification model, in this study, the context of the trained classification model is a form of the syntax that performed data training or the fitting process. The results of this fitting process will be converted into test data in the testing process.
To plot the prediction results in a graph with the target variables 1 and 0, the sigmoid function plays a very important role in this, the equation of the sigmoid function is shown by equation 3. The confusion matrix calculates and provides a classification model regarding correctness of results and what types of errors are made. Based on Figure 5 above, precision, recall, precision and F-score values can be calculated sequentially in the following way.
In this study, k-fold = 10 was used where 10-fold and 20-fold cross-validation was the most commonly recommended procedure to check the generalizability of the model [9]. Iterating or folding to 1, that is, the first part becomes the test set, the remaining part becomes the training data set, then the system will calculate the accuracy. In the second fold, where the second part becomes the test set, the other part becomes the training set, then the accuracy is recalculated.
While the Area Under Curve (AUC) value is a number that shows the area under the curve of the ROC curve. RMSE is one of the indicators used to evaluate the average error of the algorithm used, or what is called the standard error algorithm. The smaller the RMSE value of the algorithm, the better the performance of the algorithm in predicting diagnosis [22].
RESULT AND ANALYSIS
ROC curve is a technique to visualize and test the performance of classifiers based on their performance. Based on the confusion matrix above, the accuracy, precision, recall and F-1 scores of the test data can be generated, which are shown in Table 4. Based on Table 4 above, the logistic regression algorithm has a relatively high test data accuracy value of 0.965 or the accuracy of predicting the type of breast cancer with an accuracy of 96.5%.
In addition, logistic regression can provide information according to the required data (precision), which is the best, namely 95.7%, with an information retrieval ability (recall) of 95.6%, so it has a harmony or good score of F-1, and otherwise 95.6%. The next evaluation method, ROC-AUC, is used to measure the algorithm's ability to distinguish predictions as 1 and 0. Based on the above curve, it can be analyzed that the model can distinguish or separate the data as predictions 0 and 1. namely 96.
The algorithm error value is obtained by the root mean error square (RMSE) method, which is 0.19. RMSE shows the error value of the algorithm in the prediction process, based on the square root difference of the predicted value and the actual value squared and divided by the number of data.
CONCLUSION
ACKNOWLEDGEMENTS
