scholarly journals CLASSIFICATION OF HEAD AND NECK CANCER TYPES USING MACHINE LEARNING ALGORITHM

2020 ◽  
pp. 198-205
Author(s):  
Prof O. Olabode ◽  
Prof A. O. Adetunmbi ◽  
Folake Akinbohun ◽  
Dr Ambrose Akinbohun
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Naive Bayes ◽  
Information Gain ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Chi Square

The worldwide incidence of head and neck cancer exceeds half a million cases annually. The morbidity and mortality of head and neck cancers considering thyroid, nasopharyngeal, sinonasal and laryngeal were reported high. The degree of facial disfigurement is unrivalled. Information Gain and Chi Square, Decision and Naïve Bayes were deployed for the study. The dataset was divided into training and test data. The results showed that the performance of Naïve Bayes outperformed Decision Trees. With the application of machine learning algorithms, head and neck cancer can be classified. KEYWORDS: Head and Neck, thyroid, Chi Square, Information Gain

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals Classification of forest fire areas using machine learning algorithm

2021 ◽  
Vol 3 (1) ◽  
pp. 008-015
Author(s):  
Saruni Dwiasnati ◽  
Yudo Devianto
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Fire Hazard ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Burned Area ◽  
K Nearest Neighbor

Forest fires that occur will cause various kinds of problems, both in terms of health, such as smoke that can interfere with the respiratory system, in terms of the economy such as the economic wheel cannot run as usual, in terms of the environment can damage the surrounding environment and the environment that is missed by smoke, and other disasters. Forest fires can also have an impact on the costs that will be incurred to resolve the problems that arise due to forest fires, so research is needed to find out and measure the area affected by forest fires that burned in the range of 1980 - 2019 using a dataset of approximately 10,000. The target in this research is to be able to generate the best percentage scenario and find out the model of using the algorithm used to explore the algorithm in the Machine Learning method for the model for estimating the area of forest fires, namely the Siak Kampar Peninsula in Riau Province. In this study, 7 parameters were used to create a forest and land fire hazard map, namely weather temperature, Burned Area Density, hotspot density, wind speed, land cover type, rainfall, and land use. The seven parameters will be searched for accuracy results using the Classification method with Machine Learning algorithms, including Naïve Bayes, SVM, and K-Nearest Neighbor (K-NN). In this study, comparisons were made to obtain the best algorithm for estimating forest fire areas. By generating each algorithm is 71.72% for the Naïve Bayes algorithm, 75.00% for the SVM algorithm, and 64.71% for the K-NN algorithm.

scholarly journals A Machine Learning Framework for Improving Classification Performance on Credit Approval

2021 ◽  
Vol 10 (1) ◽  
pp. 47-52
Author(s):  
Pulung Hendro Prastyo ◽  
Septian Eko Prasetyo ◽  
Shindy Arti
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Information Gain ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Credit Scoring ◽  
Research Work ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Bayes Algorithm

Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.

scholarly journals Application of Machine Learning for Diagnosis of Head and Neck Cancer in Primary Healthcare Organisation

2020 ◽  
Vol 5 (4) ◽  
pp. 489-493
Author(s):  
Olatunbosun Olabode ◽  
Adebayo O. Adetunmbi ◽  
Folake Akinbohun ◽  
Ambrose Akinbohun
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Decision Tree ◽  
Head And Neck ◽  
Neck Cancer ◽  
Learning Algorithm ◽  
Late Presentation ◽  
Healthcare Organisation ◽  
K Nearest Neighbors ◽  
Supervised Learning Algorithms

Head and neck cancers (HNC) are indicated when cells grow abnormally.  The disturbing rate of morbidity and mortality of patients with HNC due to late presentation is on the increase especially in Africa (developing countries). There is need to diagnose head and neck cancer early if patients present so that prompt referral could be facilitated.  The collected data consists of 1473 instances with 18 features. The dataset was divided into training and test data.  Two supervised learning algorithms were deployed for the study namely: Decision Tree (C4.5) and k-Nearest Neighbors (KNN). It showed that Decision Tree outperformed with accuracy of 91.40% while KNN had accuracy of 88.24%. Hence, machine learning algorithm like Decision Tree can be used for diagnosis of HNC in healthcare organisations.

Patient classification of two-week wait referrals for suspected head and neck cancer: a machine learning approach

2019 ◽  
Vol 133 (10) ◽  
pp. 875-878 ◽  
Author(s):  
J W Moor ◽  
V Paleri ◽  
J Edwards
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Learning Algorithms ◽  
False Negative ◽  
Machine Learning Algorithms ◽  
Patient Classification ◽  
Cancer Symptoms ◽  
Machine Learning Classification

AbstractBackgroundMachine learning algorithms could potentially be used to classify patients referred on the two-week wait pathway for suspected head and neck cancer. Patients could be classified into ‘predicted cancer’ or ‘predicted non-cancer’ groups.MethodsA variety of machine learning algorithms were assessed using the clinical data of 5082 patients. These patients had previously been referred via the two-week wait pathway for suspected head and neck cancer to two separate tertiary referral centres in the UK. Outcomes from machine learning classification were analysed in comparison to known clinical diagnoses.ResultsVariational logistic regression was the most clinically useful technique of those chosen to perform the analysis and patient classification; the proportion of patients correctly classified as having ‘non-cancer’ was 25.8 per cent, with a false negative rate of 1 out of 1000.ConclusionMachine learning algorithms can accurately and effectively classify patients referred with suspected head and neck cancer symptoms.

scholarly journals Enabling Accurate Indoor Localization Using a Machine Learning Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 96
Author(s):  
Haidar Abdulrahman Abbas ◽  
Kayhan Zrar Ghafoor
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Excellent Result ◽  
Indoor Positioning ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Support Vector ◽  
Indoor Environments

In this paper, fingerprint referencing methods based on wireless fidelity Wi-Fi received signal strength (RSS) have used for indoor positioning. More precisely, Naïve Bayes, decision tree (DT), and support vector machine (SVM) one-to-one multi-classes and error-correcting-output-codes classifier are to enable accurate indoor positioning. Then, normalization is used to reduce positioning error by reducing the fluctuation and diverse distribution of the RSS values. Different devices are used in this experiment; the training dataset is not included in the main dataset. Nonetheless, the learned model by the SVM algorithm cannot be affected by the elimination of train datasets of the test device. The efficiency of DT is lower than the other machine learning algorithms, because it performs by Boolean function, and it provides the low accuracy of prediction for dataset than the algorithms. Naïve Bayes technique based on Bayes Theorem is better than DT and close to SVM for positioning approves that 1–1.5 m positioning accuracy for indoor environments can be achieved by the proposed approach which is an excellent result than traditional protocol.

Osteomyocutaneous Free Fibula Flap Prevents Osteoradionecrosis and Osteomyelitis in Head and Neck Cancer Reconstruction

2021 ◽  
Author(s):  
Kuan-Ying Wang ◽  
Wen-Chung Liu ◽  
Chun-Feng Chen ◽  
Lee-Wei Chen ◽  
Hung-Chi Chen ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Muscle Flap ◽  
Flexor Hallucis Longus ◽  
Free Fibula Flap ◽  
Chi Square ◽  
Fibula Flap ◽  
Free Fibula ◽  
Chi Square Test

Abstract Background Osteoradionecrosis (ORN) is one of the most severe complications of free fibula reconstruction after radiotherapy. The gold standard treatment of osteomyelitis involves extensive debridement, antibiotics, and sufficiently vascularized muscle flap coverage for better circulation. Therefore, we hypothesized that free fibula flap with muscle could decrease the risk of ORN. Methods This study consisted of 85 patients who underwent reconstruction with free fibula flap in head and neck cancer by a single reconstructive surgeon at Kaohsiung Veterans General Hospital over a period of 19 years (1998–2016). Patients with postoperative adjuvant radiotherapy were included in the study and were grouped by either free fibula osteocutaneous flap or free fibula osteomyocutaneous flap (with flexor hallucis longus muscle), and the incidence of ORN was compared. Results Of the 85 patients, 15 were reconstructed with osteocutaneous fibula flap and 70 were with osteomyocutaneous fibula flap. The rate of ORN or osteomyelitis was significantly lower in the muscle group (18.6%, n = 13/70 vs. 46.7%, n = 7/15, p = 0.020, Chi-square test). Conclusion Vascularized muscle transfer increases perfusion of surrounding tissues and the bone flap, thereby decreasing the incidence of osteomyelitis or osteonecrosis.

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