scholarly journals Implementing Classification Algorithms for Predicting Chronic Diabetes Diseases

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1748-1751
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Naive Bayes ◽  
Early Stage ◽  
Food Habit ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pima Indians ◽  
Sugar Levels

Now a day Chronic Diabetes Disease is increasing due to many reasons like changes in life style, food habit. It causes an increase in blood sugar levels. If Diabetes Disease remains untreated or unidentified, many different types of complications may be occurred. The doctors have the problem to identify these kinds of diseases easily. The machine learning algorithms helps the doctor to solve these types of problems. In this paper, we implemented three algorithms namely logistic regression, Naive Bayes and Decision tree algorithms to predict diabetes at an early stage. Experiments are performed on Pima Indians Diabetes Dataset, which is from UCI machine learning repository. The performance of all the three algorithms is evaluated using measures on Accuracy. Results obtained showed logistic regression displays 75.3%, Decision tree displays 77.9% and Naive Bayes classifier displays the accuracy value is 76.6%.

scholarly journals Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Nasopharyngeal Cancer ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tumor Diameter ◽  
Survival Prognosis ◽  
Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

scholarly journals Future Prediction of Diabetics using XG Booster Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 2128-2132
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Common Disease ◽  
Data Set ◽  
Glucose Content

Diabetes is a most common disease that occurs to most of the humans now a day. The predictions for this disease are proposed through machine learning techniques. Through this method the risk factors of this disease are identified and can be prevented from increasing. Early prediction in such disease can be controlled and save human’s life. For the early predictions of this disease we collect data set having 8 attributes diabetic of 200 patients. The patients’ sugar level in the body is tested by the features of patient’s glucose content in the body and according to the age. The main Machine learning algorithms are Support vector machine (SVM), naive bayes (NB), K nearest neighbor (KNN) and Decision Tree (DT). In the exiting the Naive Bayes the accuracy levels are 66% but in the Decision tree the accuracy levels are 70 to 71%. The accuracy levels of the patients are not proper in range. But in XG boost classifiers even after the Naïve Bayes 74 Percentage and in Decision tree the accuracy levels are 89 to 90%. In the proposed system the accuracy ranges are shown properly and this is only used mostly. A dataset of 729 patients can be stored in Mongo DB and in that 129 patients repots are taken for the prediction purpose and the remaining are used for training. The training datasets are used for the prediction purposes.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

scholarly journals Data Driven Approach for Eye Disease Classification with Machine Learning

2019 ◽  
Vol 9 (14) ◽  
pp. 2789 ◽  
Author(s):  
Sadaf Malik ◽  
Nadia Kanwal ◽  
Mamoona Naveed Asghar ◽  
Mohammad Ali A. Sadiq ◽  
Irfan Karamat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Multiple Features ◽  
Standard Format ◽  
Free Data

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

scholarly journals Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Amirhessam Tahmassebi ◽  
Amir H. Gandomi ◽  
Mieke H. J. Schulte ◽  
Anna E. Goudriaan ◽  
Simon Y. Foo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Decision Tree ◽  
Data Reduction ◽  
Resting State ◽  
Naive Bayes ◽  
Resting State Fmri ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Bayes Classifier

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

scholarly journals IProCAD: Intelligent Prognosis of Coronary Artery Disease Excluding Angiogram in Patient with Stable Angina

2020 ◽  
Vol 9 (5) ◽  
pp. 2032-2040
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Stable Angina ◽  
Naive Bayes ◽  
Feature Vector ◽  
Naïve Bayes ◽  
The Other

Cardiovascular diseases are one of the main causes of mortality in the world. A proper prediction mechanism system with reasonable cost can significantly reduce this death toll in the low-income countries like Bangladesh. For those countries we propose machine learning backed embedded system that can predict possible cardiac attack effectively by excluding the high cost angiogram and incorporating only twelve (12) low cost features which are age, sex, chest pain, blood pressure, cholesterol, blood sugar, ECG results, heart rate, exercise induced angina, old peak, slope, and history of heart disease. Here, two heart disease datasets of own built NICVD (National Institute of Cardiovascular Disease, Bangladesh) patients’, and UCI (University of California Irvin) are used. The overall process comprises into four phases: Comprehensive literature review, collection of stable angina patients’ data through survey questionnaires from NICVD, feature vector dimensionality is reduced manually (from 14 to 12 dimensions), and the reduced feature vector is fed to machine learning based classifiers to obtain a prediction model for the heart disease. From the experiments, it is observed that the proposed investigation using NICVD patient’s data with 12 features without incorporating angiographic disease status to Artificial Neural Network (ANN) shows better classification accuracy of 92.80% compared to the other classifiers Decision Tree (82.50%), Naïve Bayes (85%), Support Vector Machine (SVM) (75%), Logistic Regression (77.50%), and Random Forest (75%) using the 10-fold cross validation. To accommodate small scale training and test data in our experimental environment we have observed the accuracy of ANN, Decision Tree, Naïve Bayes, SVM, Logistic Regression and Random Forest using Jackknife method, which are 84.80%, 71%, 75.10%, 75%, 75.33% and 71.42% respectively. On the other hand, the classification accuracies of the corresponding classifiers are 91.7%, 76.90%, 86.50%, 76.3%, 67.0% and 67.3%, respectively for the UCI dataset with 12 attributes. Whereas the same dataset with 14 attributes including angiographic status shows the accuracies 93.5%, 76.7%, 86.50%, 76.8%, 67.7% and 69.6% for the respective classifiers

scholarly journals Hate Speech Detection in Portuguese with Naïve Bayes, SVM, MLP and Logistic Regression

2020 ◽  
Author(s):  
Adriano Silva ◽  
Norton Roman
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Logistic Regression ◽  
Hate Speech ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Speech Detection ◽  
Related Literature ◽  
Automatic Methods

Even though social networks can provide free space for discussing ideas, people can also use them to propagate hate speech and, given the amount of written material in such networks, it becomes necessary to rely on automatic methods for identifying this problem. In this work, we set out to verify the use of some classic Machine Learning algorithms for the task of hate speech detection in tweets written in Portuguese, by testing four different models (SVM, MLP, Logistic Regression and Naïve Bayes) with different configurations. Results show that these algorithms produce better results (in terms of micro-averaged F1 score) than the LSTM used for benchmark, being also competitive to other results by the related literature

scholarly journals Performance Evaluation of Machine Learning Models for Diabetes Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 1976-1980 ◽  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Adult Population ◽  
International Diabetes Federation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Pima Indians ◽  
K Nearest Neighbor

Diabetes is one of the prevalent diseases all over the world. As per the International Diabetes Federation (IDF) report of the year 2017, diabetes is prevalent in about 8.8% of the Indian adult population and is one of the top ten causes of death in India. In untreated and unidentified diabetes could cause fluctuations in the sugar levels and extreme cases, damage organs such as kidneys, eyes, and arteries in the heart. By using Machine learning algorithms to predict the disease from the relevant datasets at an early stage could likely save human lives. The purpose of this investigation is to assess the classifiers that can predict the probability of disease in patients with the greatest precision and accuracy. Experimental work has been carried out using classification algorithms such as K Nearest Neighbor (KNN), Decision Tree(DT), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest(RF) on Pima Indians Diabetes dataset using nine attributes which is available online on UCI Repository. The performance of classifier is evaluated based on precision, recall, accuracy and is estimated over correct and incorrect instances. The results proved that Logistic Regression (LR) performs better with the accuracy of 77.6 % in comparison to other algorithms

scholarly journals Classification of Student Performance Dataset using Machine Learning Algorithms

2019 ◽  
Vol 9 (2S2) ◽  
pp. 752-757
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Student Performance ◽  
Performance Metrics ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

The scope of this research work is to identify the efficient machine learning algorithm for predicting the behavior of a student from the student performance dataset. We applied Support Vector Machines, K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms to predict the grade of a student and compared their prediction results in terms of various performance metrics. The students who visited many resources for reference, made academic related discussions and interactions in the class room, absent for minimum days, cared by parents care have shown great improvement in the final grade. Among the machine learning techniques we have used, SVM has shown more accuracy in terms of four important attribute. The accuracy rate of SVM after tuning is 0.80. The KNN and decision tree achieves the accuracy of 0.64, 0.65 respectively whereas the Naïve Bayes achieves 0.77.

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