scholarly journals Aplikasi Diagnosis Penyakit Kanker Payudara Menggunakan Algoritma Sequential Minimal Optimization

2017 ◽  
Vol 5 (4) ◽  
pp. 153
Author(s):  
Agung Wibowo
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Sequential Minimal Optimization ◽  
Diagnose Breast Cancer ◽  
Web Based ◽  
Test Dataset ◽  
Smo Algorithm ◽  
Increase In Accuracy ◽  
Model Formation ◽  
Fold Cross Validation

Various methods for the diagnosis of breast cancer exist, but not many have been implemented as an application. This study aims to develop an application using SMO algorithm assisted by Weka to diagnose breast cancer. The application was web-based application and developed using Javascript. Test dataset and model formation used original Breast Cancer Database (WBCD) data without missing value. Test mode used 10-fold cross-validation. This application can diagnose breast cancer with an accuracy of 97.3645% and has a significant increase in accuracy for the diagnosis of malignant cancer.Beragam metode untuk diagnosis kanker payudara, namun belum banyak yang diimplementasikan menjadi sebuah aplikasi. Penelitian ini bertujuan untuk mengembangkan aplikasi berdasarkan model hasil kalkulasi algoritma SMO berbantuan Weka untuk mendiagnosis penyakit kanker payudara. Aplikasi dikembangkan berbasis web menggunakan Javascript. Dataset pengujian dan pembentukan model menggunakan data Winconsin Breast Cancer Database original (WBCD) tanpa nilai hilang. Mode pengujian menggunakan 10-fold cross validation. Aplikasi ini dapat mendiagnosis kanker payudara dengan akurasi 97.3645% dan memiliki peningkatan akurasi yang signifikan untuk diagnosis kanker ganas.

scholarly journals PENERAPAN METODE SELEKSI FITUR UNTUK MENINGKATKAN HASIL DIAGNOSIS KANKER PAYUDARA

2016 ◽  
Vol 7 (1) ◽  
pp. 283 ◽  
Author(s):  
Elvira Sukma Wahyuni
Keyword(s):  
Breast Cancer ◽  
Rough Set ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Sequential Minimal Optimization ◽  
Multi Layer Perceptron ◽  
Fold Cross Validation

Tujuan utama penelitian ini adalah untuk meningkatkan peforma klasifikasi pada diagnosis kanker payudara dengan menerapkan seleksi fitur pada beberapa algoritme klasifikasi. Penelitian ini menggunakan database kanker payudara Wisconsin Breast Cancer Database (WBCD). Metode seleksi fitur F-score dan Rough Set akan dipasangkan dengan beberapa algoritme klasifikasi yaitu SMO (Sequential Minimal Optimization), Naive Bayes, Multi layer Perceptron, dan C4.5. Penelitian ini menggunakan 10 fold cross validation sebagai metode evaluasi. Hasil penelitian menunjukkan algoritme klasifikasi MLP dan C4.5 mengalami peningkatan peforma klasifikasi secara signifikan setelah dipasangkan dengan seleksi fitur rough set dan F-score, Naive Bayes menunjukan peforma terbaik ketika dipasangkan dengan metode seleksi fitur F-score saja, sedangkan SMO tidak menunjukkan peningkatan peforma klasifikas ketika dipasangkan pada kedua seleksi fitur. Kata kunci: kanker payudara, seleksi fitur, klasifikasi.

scholarly journals Recognizing ion ligand binding sites by SMO algorithm

2019 ◽  
Vol 20 (S3) ◽  
Author(s):  
Shan Wang ◽  
Xiuzhen Hu ◽  
Zhenxing Feng ◽  
Xiaojin Zhang ◽  
Liu Liu ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Sites ◽  
Protein Function ◽  
Metal Ion ◽  
Cross Validation ◽  
Sequence Information ◽  
Sequential Minimal Optimization ◽  
Ligand Binding Sites ◽  
Smo Algorithm ◽  
Fold Cross Validation

Abstract Background In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results In this study, four acid radical ion ligands (NO2−,CO32−,SO42−,PO43−) and ten metal ion ligands (Zn2+,Cu2+,Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions An efficient method for predicting ion ligand binding sites was presented.

Breast Cancer Detection Using Image Processing and CNN Algorithm with K-Fold Cross-Validation

2021 ◽  
pp. 481-490
Author(s):  
Pruthvi Tilekar ◽  
Purnima Singh ◽  
Nagnath Aherwadi ◽  
Sagar Pande ◽  
Aditya Khamparia
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Cancer Detection ◽  
Cross Validation ◽  
Breast Cancer Detection ◽  
Fold Cross Validation

scholarly journals Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuhong Huang ◽  
Wenben Chen ◽  
Xiaoling Zhang ◽  
Shaofu He ◽  
Nan Shao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cross Validation ◽  
Learning Model ◽  
Training Dataset ◽  
Tumor Shrinkage ◽  
Clinicopathologic Characteristics ◽  
Testing Dataset ◽  
Machine Learning Model ◽  
Fold Cross Validation

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

scholarly journals Traditional Chinese Medicine (TCM) Diagnosis Model Building Based on Multi-label Classification

2018 ◽  
Vol 232 ◽  
pp. 02026
Author(s):  
Lu Zhou ◽  
Guang-geng Li ◽  
Yu-mei Zhou ◽  
Dan Yin ◽  
Yan Sun ◽  
...  
Keyword(s):  
Cross Validation ◽  
Model Building ◽  
Learning Algorithm ◽  
Training Data ◽  
Average Precision ◽  
Test Dataset ◽  
Average Accuracy ◽  
Good Potential ◽  
Diagnosis Model ◽  
Fold Cross Validation

In the study, we propose a TCM diagnosis model that can be used for multi-label classification and give clear diagnosis, as well as the basis for diagnosis and differentiation when the symptoms correspond to multiple diseases or syndromes. The implementation of the model is divided into three steps. Firstly, choose the machine learning algorithm to train the TCM diagnosis model. The features of the training data are symptoms and the labels are diseases or syndromes. Secondly, give the number α (α>1, α∈Z+), the model will output the diagnoses with the top α highest probability according to the input symptoms as candidate diagnoses. Finally, the rules of differential diagnosis are designed to determine which candidate diagnoses should be reserved, thereby complete the multi-label classification. In our test dataset, by 10-fold cross-validation, the average accuracy of the single label classification was 0.882; the average precision was 0.974; the average recall was 1.000; the average f1 score was 0.967; the average accuracy of the multi-label classification was 0.706; the average micro precision was 0.934; the average micro recall was 0.941 and the average hamming loss was 0.060. Through the test we can know that this model had a good potential for auxiliary decision making in clinical diagnosis and treatment.

scholarly journals Analyzing performance of classifiers for medical datasets

2018 ◽  
Vol 7 (2.15) ◽  
pp. 136 ◽  
Author(s):  
Rosaida Rosly ◽  
Mokhairi Makhtar ◽  
Mohd Khalid Awang ◽  
Mohd Isa Awang ◽  
Mohd Nordin Abdul Rahman
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Ensemble Methods ◽  
Data Sets ◽  
Ensemble Classifiers ◽  
Classification Models ◽  
Data Set ◽  
Mining Tool ◽  
Fold Cross Validation

This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.  

scholarly journals Origin of aromatase inhibitory activity via proteochemometric modeling

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1979 ◽  
Author(s):  
Saw Simeon ◽  
Ola Spjuth ◽  
Maris Lapins ◽  
Sunanta Nabu ◽  
Nuttapat Anuwongcharoen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Inhibitory Activity ◽  
Cross Validation ◽  
External Validation ◽  
Predictive Performance ◽  
Interaction Space ◽  
Inhibitory Mechanisms ◽  
Rate Limiting ◽  
Good Predictive Performance ◽  
Fold Cross Validation

Aromatase, the rate-limiting enzyme that catalyzes the conversion of androgen to estrogen, plays an essential role in the development of estrogen-dependent breast cancer. Side effects due to aromatase inhibitors (AIs) necessitate the pursuit of novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a novel therapeutic agent against aromatase could be achieved computationally by means of ligand-based and structure-based methods. For over a decade, we have utilized both approaches to design potential AIs for which quantitative structure–activity relationships and molecular docking were used to explore inhibitory mechanisms of AIs towards aromatase. However, such approaches do not consider the effects that aromatase variants have on different AIs. In this study, proteochemometrics modeling was applied to analyze the interaction space between AIs and aromatase variants as a function of their substructural and amino acid features. Good predictive performance was achieved, as rigorously verified by 10-fold cross-validation, external validation, leave-one-compound-out cross-validation, leave-one-protein-out cross-validation and Y-scrambling tests. The investigations presented herein provide important insights into the mechanisms of aromatase inhibitory activity that could aid in the design of novel potent AIs as breast cancer therapeutic agents.

scholarly journals Deep Learning Based Pectoral Muscle Segmentation on MIAS Mammograms

2020 ◽  
Author(s):  
Young Jae Kim ◽  
Eun Young Yoo ◽  
Kwang Gi Kim
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Deep Learning ◽  
Cross Validation ◽  
Pectoral Muscle ◽  
Training Data ◽  
Dice Similarity Coefficient ◽  
Detection Accuracy ◽  
Pectoral Muscle Detection ◽  
Fold Cross Validation

Abstract Background: The purpose of this study was to propose a deep learning-based method for automated detection of the pectoral muscle, in order to reduce misdetection in a computer-aided diagnosis (CAD) system for diagnosing breast cancer in mammography. This study also aimed to assess the performance of the deep learning method for pectoral muscle detection by comparing it to an image processing-based method using the random sample consensus (RANSAC) algorithm. Methods: Using the 322 images in the Mammographic Image Analysis Society (MIAS) database, the pectoral muscle detection model was trained with the U-Net architecture. Of the total data, 80% was allocated as training data and 20% was allocated as test data, and the performance of the deep learning model was tested by 5-fold cross validation. Results: The image processing-based method for pectoral muscle detection using RANSAC showed 92% detection accuracy. Using the 5-fold cross validation, the deep learning-based method showed a mean sensitivity of 95.55%, mean specificity of 99.88%, mean accuracy of 99.67%, and mean Dice similarity coefficient (DSC) of 95.88%. Conclusions: The proposed deep learning-based method of pectoral muscle detection performed better than an existing image processing-based method. In the future, by collecting data from various medical institutions and devices to further train the model and improve its reliability, we expect that this model could greatly reduce misdetection rates by CAD systems for breast cancer diagnosis.

scholarly journals Ensemble Feature Selection for Breast Cancer Classification using Microarray Data

2020 ◽  
Vol 23 (65) ◽  
pp. 100-114
Author(s):  
Supoj Hengpraprohm ◽  
Suwimol Jungjit
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cross Validation ◽  
Microarray Dataset ◽  
Classification Model ◽  
Cancer Data ◽  
Evaluation Functions ◽  
Selection For ◽  
Feature Selection Approach ◽  
Fold Cross Validation

For breast cancer data classification, we propose an ensemble filter feature selection approach named ‘EnSNR’. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification ‘model’. The efficiency of the ‘model’ is validated using 10-Fold Cross-Validation re-sampling. The Microarray dataset used in our experiments contains 50,739 genes for each of 32 patients. When our proposed ‘EnSNR’ subset of features is used; as well as giving an enhanced degree of prediction accuracy and reducing the number of irrelevant features (genes), there is also a small saving of computer processing time.

scholarly journals Deteksi Penyakit Kanker Payudara dengan Seleksi Fitur berbasis Principal Component Analysis dan Random Forest

2020 ◽  
Vol 2 (1) ◽  
pp. 96-101
Author(s):  
Ahmad Fauzi ◽  
Riki Supriyadi ◽  
Nurlaelatul Maulidah
Keyword(s):  
Breast Cancer ◽  
Principal Component Analysis ◽  
Random Forest ◽  
Cross Validation ◽  
Principal Component ◽  
Component Analysis ◽  
Data Set ◽  
Fold Cross Validation

Abstrak  - Skrining merupakan upaya deteksi dini untuk mengidentifikasi penyakit atau kelainan yang secara klinis belum jelas dengan menggunakan tes, pemeriksaan atau prosedur tertentu. Upaya ini dapat digunakan secara cepat untuk membedakan orang - orang yang kelihatannya sehat tetapi sesungguhnya menderita suatu kelainan.Tujuan utama penelitian ini adalah untuk meningkatkan peforma klasifikasi pada diagnosis kanker payudara dengan menerapkan seleksi fitur pada beberapa algoritme klasifikasi. Penelitian ini menggunakan database kanker payudara Breast Cancer Coimbra Data Set . Metode seleksi fitur berbasis pricipal component analysis akan dipasangkan dengan beberapa algoritme klasifikasi dan metode, seperti Logitboost,Bagging,dan Random Forest. Penelitian ini menggunakan 10 fold cross validation sebagai metode evaluasi. Hasil penelitian menunjukkan metode seleksi fitur berbasis pricipal component analysis mengalami peningkatan peforma klasifikasi secara signifikan setelah dipasangkan dengan seleksi fitur Random Forest dan logitboost, Random forest menunjukan peforma terbaik dengan akurasi 79.3103% dengan nilai AUC sebesar 0,843. Kata Kunci: Seleksi Fitur,PCA, Kanker Payudara,Skrining,Random Forest

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