scholarly journals Heart Disease Prediction Model Using Naïve Bayes Algorithm and Machine Learning Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 46
Author(s):  
Maria Yousef ◽  
Prof. Khaled Batiha
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Heart Disease ◽  
Prediction Model ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Prediction Systems

These days, heart disease comes to be one of the major health problems which have affected the lives of people in the whole world. Moreover, death due to heart disease is increasing day by day. So the heart disease prediction systems play an important role in the prevention of heart problems. Where these prediction systems assist doctors in making the right decision to diagnose heart disease easily. The existing prediction systems suffering from the high dimensionality problem of selected features that increase the prediction time and decrease the performance accuracy of the prediction due to many redundant or irrelevant features. Therefore, this paper aims to provide a solution of the dimensionality problem by proposing a new mixed model for heart disease prediction based on (Naïve Bayes method, and machine learning classifiers).In this study, we proposed a new heart disease prediction model (NB-SKDR) based on the Naïve Bayes algorithm (NB) and several machine learning techniques including Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest. This prediction model consists of three main phases which include: preprocessing, feature selection, and classification. The main objective of this proposed model is to improve the performance of the prediction system and finding the best subset of features. This proposed approach uses the Naïve Bayes technique based on the Bayes theorem to select the best subset of features for the next classification phase, also to handle the high dimensionality problem by avoiding unnecessary features and select only the important ones in an attempt to improve the efficiency and accuracy of classifiers. This method is able to reduce the number of features from 13 to 6 which are (age, gender, blood pressure, fasting blood sugar, cholesterol, exercise induce engine) by determining the dependency between a set of attributes. The dependent attributes are the attributes in which an attribute depends on the other attribute in deciding the value of the class attribute. The dependency between attributes is measured by the conditional probability, which can be easily computed by Bayes theorem. Moreover, in the classification phase, the proposed system uses different classification algorithms such as (DT Decision Tree, RF Random Forest, SVM Support Vector machine, KNN Nearest Neighbors) as a classifiers for predicting whether a patient has heart disease or not. The model is trained and evaluated using the Cleveland Heart Disease database, which contains 13 features and 303 samples.Different algorithms use different rules for producing different representations of knowledge. So, the selection of algorithms to build our model is based on their performance. In this work, we applied and compared several classification algorithms which are (DT, SVM, RF, and KNN) to identify the best-suited algorithm to achieve high accuracy in the prediction of heart disease. After combining the Naive Bayes method with each one of these previous classifiers the performance of these combines algorithms is evaluated by different performance metrics such as (Specificity, Sensitivity, and Accuracy). Where the experimental results show that out of these four classification models, the combination between the Naive Bayes feature selection approach and the SVM RBF classifier can predict heart disease with the highest accuracy of 98%. Finally, the proposed approach is compared with another two systems which developed based on two different approaches in the feature selection step. The first system, based on the Genetic Algorithm (GA) technique, and the second uses the Principal Component Analysis (PCA) technique. Consequently, the comparison proved that the Naive Bayes selection approach of the proposed system is better than the GA and PCA approach in terms of prediction accuracy.   

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 700-704
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Nearest Neighbour ◽  
Decision Tree Classifier ◽  
Support Vector Classifier ◽  
Learning Techniques ◽  
Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

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.

Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile

2021 ◽  
Vol 6 (3) ◽  
pp. 35-47
Author(s):  
Anirudh Reddy Cingireddy ◽  
Robin Ghosh ◽  
Supratik Kar ◽  
Venkata Melapu ◽  
Sravanthi Joginipeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Albert Einstein ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Blood Profile ◽  
Molecular Tests ◽  
Large Populations

Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.

scholarly journals Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction

2020 ◽  
Vol 7 (2) ◽  
pp. 631-647
Author(s):  
Emrana Kabir Hashi ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Traditional System ◽  
Prediction Approach

Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.

scholarly journals Prediction of Heart Disease using Machine Learning

2019 ◽  
Vol 8 (2S10) ◽  
pp. 474-477
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Support Vector Machines ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Set ◽  
Vector Machines ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Machine learning is one of the fast growing aspect in current world. Machine learning (ML) and Artificial Neural Network (ANN) are helpful in detection and diagnosis of various heart diseases. Naïve Bayes Classification is a vital approach of classification in machine learning. The heart disease consists of set of range disorders affecting the heart. It includes blood vessel problems such as irregular heart beat issues, weak heart muscles, congenital heart defects, cardio vascular disease and coronary artery disease. Coronary heart disorder is a familiar type of heart disease. It reduces the blood flow to the heart leading to a heart attack. In this paper the UCI machine learning repository data set consisting of patients suffering from heart disease is analyzed using Naïve Bayes classification and support vector machines. The classification accuracy of the patients suffering from heart disease is predicted using Naïve Bayes classification and support vector machines. Implementation is done using R language.

scholarly journals A comparison of three discrete methods for classification of heart disease data

2015 ◽  
Vol 50 (4) ◽  
pp. 293-296 ◽  
Author(s):  
D Chaki ◽  
A Das ◽  
MI Zaber
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

The classification of heart disease patients is of great importance in cardiovascular disease diagnosis. Numerous data mining techniques have been used so far by the researchers to aid health care professionals in the diagnosis of heart disease. For this task, many algorithms have been proposed in the previous few years. In this paper, we have studied different supervised machine learning techniques for classification of heart disease data and have performed a procedural comparison of these. We have used the C4.5 decision tree classifier, a naïve Bayes classifier, and a Support Vector Machine (SVM) classifier over a large set of heart disease data. The data used in this study is the Cleveland Clinic Foundation Heart Disease Data Set available at UCI Machine Learning Repository. We have found that SVM outperformed both naïve Bayes and C4.5 classifier, giving the best accuracy rate of correctly classifying highest number of instances. We have also found naïve Bayes classifier achieved a competitive performance though the assumption of normality of the data is strongly violated.Bangladesh J. Sci. Ind. Res. 50(4), 293-296, 2015

scholarly journals A Comprehensive Performance Analysis of Various Classifier Models for Coronary Artery Disease Prediction

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Vascular Diseases ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Support Vector ◽  
Disease Prediction ◽  
Weak Classifier

Cardio Vascular Diseases (CVD) is the major reason for the death of the majority of the people in the world. Earlier diagnosis of disease will reduce the mortality rate. Machine learning (ML) algorithms are giving promising results in the disease diagnosis and it is now widely accepted by medical experts as their clinical decision support system. In this work, the most popular ML models are investigated and compared with one other for heart disease prediction based on various metrics. The base classifiers such as Support Vector Machine (SVM), Logistic regression, Naïve Bayes, Decision Tree, K Nearest Neighbour are used for predicting heart disease. In this paper, bagging and boosting techniques are applied over these individual classifiers to improve the performance of the system. With the Cleveland and Statlog datasets, Naive Bayes as the individual classifier gives the maximum accuracy of 85.13%and 84.81% respectively. Bagging technique improves the accuracy of the decision tree which is identified as a weak classifier by 7% and it is a significant improvement in identifying CVD.

scholarly journals Peningkatan Performa Pendeteksian Anomali Menggunakan Ensemble Learning dan Feature Selection

2021 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Ripto Sudiyarno ◽  
Arief Setyanto ◽  
Emha Taufiq Luthfi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Ensemble Learning ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Performance Results

Intrusion detection systems (IDS) atau Sistem pendeteksian intrusi dikenal sebagai teknik yang sangat menonjol dan terkemuka untuk menemukan malicious activities pada jaringan komputer, tidak seperti firewall konvensional, IDS berbeda dalam hal pengidentifikasian serangan secara cerdas dengan pendekatan analitik seperti data mining dan teknik machine learning. Dalam beberapa dekade terakhir, ensemble learning sangat memajukan penelitian pada machine learning dan klasifikasi pola, serta menunjukan peningkatan hasil kinerja dibandingkan single classifier. Pada Penelitian ini dilakukan percobaan peningkatan nilai akurasi terhadap sistem pendeteksian anomali, pertama dilakukan klasifikasi menggunakan single classifier untuk didapati hasil nilai akurasi yang nantinya dibandingkan dengan hasil dari ensemble learning dan feature selection. Penggunaan ensemble learning bertujuan untuk mendapatkan nilai akurasi yang terbaik dari single classifier. Hasil didapatkan dari nilai confusion matrix dan akan dilakukan pengujian dengan cara membandingkan nilai kedua metode diatas. Penelitian berhasil mendapatkan nilai akurasi single classifier (naïve bayes) yaitu 77,4% dan nilai ensemble learning 96,8%. Kata Kunci— ensemble learning, nsl-kdd, naïve bayes, anomali, feature selectionIntrusion detection systems (IDS) are known as very prominent and leading techniques for finding malicious activities on computer networks, unlike conventional firewalls, IDS differs in terms of identifying attacks intelligently with analytic approaches such as machine learning techniques. In the last few decades, ensemble learning has greatly advanced research in machine learning and pattern classification it has shown an improve in performance results compared to a single classifier. In this study an attempt was made to increase the accuracy of anomalous detection systems, first by classification using a single classifier to find the results of accuracy which will be compared with the results of ensemble learning and feature selection. The use of ensemble learning aims to get the best accuracy value from a single classifier. The results are obtained from the value of the confusion matrix and will be tested by comparing the values of the two methods above. The research succeeded in getting a single classifier accuracy value of 77,4% and ensemble learning 96,8%. Keywords— ensemble learning, nsl-kdd, naïve bayes, anomali, feature selection

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.

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