Novel Mathematical Model of Breast Cancer Diagnostics Using an Associative Pattern Classification

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

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Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2019040105 ◽

2019 ◽

Vol 10 (2) ◽

pp. 98-110 ◽

Cited By ~ 3

Author(s):

Keke Zhang ◽

Lei Zhang ◽

Qiufeng Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Early Stage ◽

Back Propagation ◽

Support Vector ◽

Automatic Identification ◽

K Nearest Neighbor ◽

Data Set ◽

Leaf Disease

The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.

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Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

Sustainability ◽

10.3390/su13063497 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3497

Author(s):

Hassan Adamu ◽

Syaheerah Lebai Lutfi ◽

Nurul Hashimah Ahamed Hassain Malim ◽

Rohail Hassan ◽

Assunta Di Vaio ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Primary Objective ◽

Support Vector ◽

Standard English ◽

Emotion Classification ◽

K Nearest Neighbor ◽

The Public ◽

The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

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Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽

10.3390/diagnostics11101870 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1870

Author(s):

Yaghoub Pourasad ◽

Esmaeil Zarouri ◽

Mohammad Salemizadeh Parizi ◽

Amin Salih Mohammed

Keyword(s):

Breast Cancer ◽

High Performance ◽

Nearest Neighbor ◽

Support Vector ◽

Ultrasound Images ◽

Morphological Operations ◽

K Nearest Neighbor ◽

Diagnose Breast Cancer ◽

Premature Deaths ◽

Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

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Twitter sentiment analysis of the relocation of Indonesia's capital city

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.2352 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1620-1630

Author(s):

Edi Sutoyo ◽

Ahmad Almaarif

Keyword(s):

Support Vector Machine ◽

Sentiment Analysis ◽

Nearest Neighbor ◽

Point Of View ◽

Capital City ◽

Support Vector ◽

K Nearest Neighbor ◽

Evaluation Algorithm ◽

The Government ◽

The Many

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.

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RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp4866-4872 ◽

2019 ◽

Vol 9 (6) ◽

pp. 4866

Author(s):

Rajni Rajni ◽

Amandeep Amandeep

Keyword(s):

Nearest Neighbor ◽

Naive Bayes ◽

Early Stage ◽

Human Life ◽

Naïve Bayes ◽

Support Vector ◽

Pima Indians ◽

K Nearest Neighbor ◽

Fast Pace ◽

Bayes Algorithm

<p>Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.</p>

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Comparative analysis on bayesian classification for breast cancer problem

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v8i4.1628 ◽

2019 ◽

Vol 8 (4) ◽

Author(s):

Wan Nor Liyana Wan Hassan Ibeni ◽

Mohd Zaki Mohd Salikon ◽

Aida Mustapha ◽

Saiful Adli Daud ◽

Mohd Najib Mohd Salleh

Keyword(s):

Breast Cancer ◽

Bayesian Networks ◽

Nearest Neighbor ◽

Naive Bayes ◽

Likelihood Estimation ◽

Predictive Distribution ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbor

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.

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Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

10.21203/rs.3.rs-24305/v1 ◽

2020 ◽

Cited By ~ 3

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Amin Alqudah

Keyword(s):

Early Diagnosis ◽

Nearest Neighbor ◽

Early Stage ◽

Support Vector ◽

K Nearest Neighbor ◽

X Ray ◽

Softmax Classifier ◽

Chest X Ray ◽

Novel Coronavirus ◽

Sensitivity Specificity

Abstract Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

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Early Stage Breast Cancer Detection System using GLCM feature extraction and K-Nearest Neighbor (k-NN) on Mammography image

2018 18th International Symposium on Communications and Information Technologies (ISCIT) ◽

10.1109/iscit.2018.8587920 ◽

2018 ◽

Cited By ~ 5

Author(s):

Than Than Htay ◽

Su Su Maung

Keyword(s):

Breast Cancer ◽

Feature Extraction ◽

Cancer Detection ◽

Nearest Neighbor ◽

Detection System ◽

Early Stage ◽

Breast Cancer Detection ◽

Early Stage Breast Cancer ◽

K Nearest Neighbor ◽

Mammography Image

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Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

Insights into Advancements in Intelligent Information Technologies ◽

10.4018/978-1-4666-0158-1.ch012 ◽

2012 ◽

pp. 220-239

Author(s):

Fei-Long Chen ◽

Feng-Chia Li

Keyword(s):

Nearest Neighbor ◽

Credit Scoring ◽

Back Propagation ◽

Support Vector ◽

Data Sets ◽

K Nearest Neighbor ◽

Hybrid Features ◽

Credit Data ◽

Learning Machine ◽

Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

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