scholarly journals Predictive Tool for Dermatology Disease Diagnosis using Machine Learning Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 355-360 ◽  
Keyword(s):  
Skin Diseases ◽  
Naive Bayes ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Predictive Tool ◽  
K Nearest Neighbors ◽  
Data Mining Algorithms

Prediction of skin diseases is more complex as many diseases have the same symptoms at the early stage but may vary at the later stages while the disease becomes incurable. So we can use data mining algorithms to classify the diseases based on the input symptoms. In this paper, the best algorithm suitable for classification of data into six dermatological diseases is determined by comparison with few other algorithms. Naive Bayes tends to show higher accuracy of 99.31% , Random forest exhibits 97.80% and SVM reveals 94.35% when test size is 40% in jupyter notebook. Linear regression and K Nearest Neighbors when trained with 80% of the data displays 82.14% and 94.44% accuracy respectively. Naive Bayes can be used for the prediction of several other diseases and is best for classification of data and thus helps doctors predict the disease more accurately and with comparatively lesser time.

scholarly journals Pathological Voice Signal Analysis Using Machine Learning Based Approaches

2017 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Yahia Alemami ◽  
Laiali Almazaydeh
Keyword(s):  
Signal Analysis ◽  
Naive Bayes ◽  
Neurodegenerative Disorder ◽  
Speech Disorders ◽  
Naïve Bayes ◽  
Human Observer ◽  
Automated Classification ◽  
Predictive Tool ◽  
K Nearest Neighbors ◽  
Voice Signal

Voice signal analysis is becoming one of the most significant examination in clinical practice due to the importance of extracting related parameters to reflect the patient's health. In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease (PD). PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control. Tremor, movement and speech disorders are the main symptoms of PD. The diagnosis decision of PD is obtained by continued clinical observation which relies on expert human observer. Therefore, an additional diagnosis method is desirable for most comfortable and timely detection of PD as well as faster treatment is needed. In this study, we develop and validate automated classification algorithms, which are based on Naïve Bayes and K- Nearest Neighbors (KNN) using voice signal measurements to predict PD. According to the results, the diagnostic performance provided by the automated classification algorithm using Naïve Bayes was superior to that of the KNN and it is useful as a predictive tool for PD screening with a high degree of accuracy, approximately 93.3%.

scholarly journals A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

2020 ◽  
Author(s):  
Kyra Mikaela M. Lopez ◽  
Ma. Sheila A. Magboo
Keyword(s):  
Support Vector Machines ◽  
Invasive Ductal Carcinoma ◽  
Naive Bayes ◽  
Ductal Carcinoma ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Support Tool ◽  
Vector Machines

This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.

scholarly journals On the Analysis of Machine Learning Classifiers to Detect Traffic Congestion in Vehicular Networks

2019 ◽  
Author(s):  
Lucas Carvalho ◽  
Maycon Silva ◽  
Edimilson Santos ◽  
Daniel Guidoni
Keyword(s):  
Machine Learning ◽  
Traffic Congestion ◽  
Vehicular Networks ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Applied Machine Learning ◽  
Routing Methods

Problems related to traffic congestion and management have become common in many cities. Thus, vehicle re-routing methods have been proposed to minimize the congestion. Some of these methods have applied machine learning techniques, more specifically classifiers, to verify road conditions and detect congestion. However, better results may be obtained by applying a classifier more suitable to domain. In this sense, this paper presents an evaluation of different classifiers applied to the identification of the level of road congestion. Our main goal is to analyze the characteristics of each classifier in this task. The classifiers involved in the experiments here are: Multiple Layer Neural Network (MLP), K-Nearest Neighbors (KNN), Decision Trees (J48), Support Vector Machines (SVM), Naive Bayes and Tree Augment Naive Bayes.

scholarly journals Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

2019 ◽  
Vol 9 (9) ◽  
pp. 231 ◽  
Author(s):  
Attallah ◽  
Sharkas ◽  
Gadelkarim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gestational Age ◽  
Naive Bayes ◽  
Fetal Brain ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Operating Characteristics ◽  
Brain Abnormality

Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.

scholarly journals Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

2020 ◽  
Vol 16 (1) ◽  
pp. 155014771989521
Author(s):  
Sujata Dash ◽  
Ajith Abraham ◽  
Ashish Kr Luhach ◽  
Jolanta Mizera-Pietraszko ◽  
Joel JPC Rodrigues
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Firefly Algorithm ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Stochastic Algorithm

Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chaotic maps which will increase the diversification and intensification capability of chaos-based firefly algorithm. The objective of chaos-based maps is to select initial values of the population of fireflies and change the value of absorption coefficient so as to increase the diversity of populations and improve the search process to achieve global optima avoiding the local optima. For selecting the most discriminant features from the search space, Naïve Bayesian stochastic algorithm with kernel density estimation as learning algorithm is applied to evaluate the discriminative features from different perspectives, namely, subset size, accuracy, stability, and generalization. The experimental study of the problem established that chaos-based logistic model overshadowed other chaotic models. In addition, four widely used classifiers such as Naïve Bayes classifier, k-nearest neighbor, decision tree, and radial basis function classifier are used to prove the generalization and stability of the logistic chaotic model. As a result, the model identified as the best one and could be used as a decision making tool by clinicians to diagnose Parkinson’s disease patients.

scholarly journals An Enhanced Corpus for Arabic Newspapers Comments

2020 ◽  
Vol 17 (5) ◽  
pp. 789-798
Author(s):  
Hichem Rahab ◽  
Abdelhafid Zitouni ◽  
Mahieddine Djoudi
Keyword(s):  
Support Vector Machines ◽  
Web Sites ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Vector Machines

In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

2021 ◽  
Vol 5 (3) ◽  
pp. 527-533
Author(s):  
Yoga Religia ◽  
Amali Amali
Keyword(s):  
Feature Selection ◽  
Customer Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Point Of View ◽  
Classification Model ◽  
Passenger Satisfaction ◽  
Airline Passenger ◽  
Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

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