scholarly journals The Analysis Performance of Heart Failure Classification by Using Machine Learning Techniques

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Nurul Farhana Hamzah ◽  
◽  
Nazri Mohd Nawi ◽  
Abdulkareem A. Hezam ◽  
◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Boosted Decision Tree ◽  
Failure Classification ◽  
Artery Disease ◽  
Using Data

Heart failure means that the heart is not pumping well as normal as it should be. A congestive heart failure is a form of heart failure that involves seeking timely medical care, although the two terms are sometimes used interchangeably. Heart failure happens when the heart muscle does not pump blood as well as it can, often referred to as congestive heart failure. Some disorders, such as heart's narrowed arteries (coronary artery disease) or high blood pressure, eventually make the heart too weak or rigid to fill and pump effectively. Early detection of heart failure by using data mining techniques has gained popularity among researchers. This research uses some classification techniques for heart failure classification from medical data. This research analyzed the performance of some classification algorithms, namely Support Vector Machine (SVM), Decision Forest (DF), and Boosted Decision Tree (BDT), to classify accurately heart failure risk data as input. The best algorithm among the three is discovered for heart failure classification at the end of this research.

scholarly journals Machine Learning for Predictions in Academics

2020 ◽  
Vol 8 (5) ◽  
pp. 4624-4627
Keyword(s):  
Machine Learning ◽  
Career Path ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Classifier ◽  
Logistics Regression ◽  
Learning Techniques

In recent years, a lot of data has been generated about students, which can be utilized for deciding the career path of the student. This paper discusses some of the machine learning techniques which can be used to predict the performance of a student and help to decide his/her career path. Some of the key Machine Learning (ML) algorithms applied in our research work are Linear Regression, Logistics Regression, Support Vector machine, Naïve Bayes Classifier and K- means Clustering. The aim of this paper is to predict the student career path using Machine Learning algorithms. We compare the efficiencies of different ML classification algorithms on a real dataset obtained from University students.

scholarly journals Prediction of Liver Diseases by Using Few Machine Learning Based Approaches

2020 ◽  
pp. 85-90
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Liver Diseases ◽  
Model Building ◽  
Medical Science ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors ◽  
Learning Techniques

Advancement in medical science has always been one of the most vital aspects of the human race. With the progress in technology, the use of modern techniques and equipment is always imposed on treatment purposes. Nowadays, machine learning techniques have widely been used in medical science for assuring accuracy. In this work, we have constructed computational model building techniques for liver disease prediction accurately. We used some efficient classification algorithms: Random Forest, Perceptron, Decision Tree, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) for predicting liver diseases. Our works provide the implementation of hybrid model construction and comparative analysis for improving prediction performance. At first, classification algorithms are applied to the original liver patient datasets collected from the UCI repository. Then we analyzed features and tweaked to improve the performance of our predictor and made a comparative analysis among the classifiers. We examined that, KNN algorithm outperformed all other techniques with feature selection.

scholarly journals Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

2019 ◽  
Vol 8 (9) ◽  
pp. 1298 ◽  
Author(s):  
Giulia Lorenzoni ◽  
Stefano Santo Sabato ◽  
Corrado Lanera ◽  
Daniele Bottigliengo ◽  
Clara Minto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Health Care ◽  
Predictive Value ◽  
Machine Learning Techniques ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Heart Failure Patients ◽  
Learning Techniques ◽  
Using Data

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system.

scholarly journals A Comparative Study of Classification Techniques for P300 Speller

2020 ◽  
Vol 9 (7S) ◽  
pp. 102-106
Keyword(s):  
Machine Learning ◽  
Event Related Potential ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Interface ◽  
Support Vector ◽  
Classification Algorithms ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Learning Techniques

P300 speller in Brain Computer Interface (BCI) allows locked-in or completely paralyzed patients to communicate with humans. To achieve the performance of characterization and increase accuracy, machine learning techniques are used. The study is about an event related potential (ERP) P300 signal detection and classification using various machine learning algorithms. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) are used to classify P300 and Non-P300 signal from Electroencephalography (EEG) signal. The performance of the system is evaluated based on f1-score using BCI competition III dataset II. In our system, we used LDA and SVM classification algorithms. Both the classifiers gave 91.0% classification accuracy.

scholarly journals Advanced Machine Learning Techniques used for Deodorant Based Sentimental Anatomy

2019 ◽  
Vol 8 (4) ◽  
pp. 5813-5816
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Maximum Entropy ◽  
Source Material ◽  
Unstructured Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Vector Machines

Now a days there is lots of data floating in the life of world access i.e Internet which is unstructured data.To manage this unstructured data we are introduced some classification algorithms in machine learning to classify the data.Sentiment Analysis[5] is contextual mining of text from documents ,reviews of customers which distinguishes and concentrates emotional data in source material. Assessment API works in fourteen unique dialects .We consider the issue of grouping records not by subject, however by generally speaking slant, e.g., deciding if an audit is certain or negative. Utilizing antiperspirants surveys as information, we locate that standard AI systems absolutely beat human-delivered baselines. The AI stratagies we connected with for arrangement are Naive Bayes, maximum entropy[2] classification, and support vector machines classification algorithms for sentiment classification as on traditional topic-based categorization.[1].

scholarly journals Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Lal Hussain ◽  
Imtiaz Ahmed Awan ◽  
Wajid Aziz ◽  
Sharjil Saeed ◽  
Amjad Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
Lower Bound ◽  
Upper Bound ◽  
Predictive Value ◽  
Complex Dynamics ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Multimodal Features

The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.

scholarly journals Learning to Classify Documents According to Formal and Informal Style

2012 ◽  
Vol 8 ◽  
Author(s):  
Fadi Abu Sheikha ◽  
Diana Inkpen
Keyword(s):  
Computational Linguistics ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Medical Texts ◽  
Learning Techniques ◽  
Sentence Level ◽  
Vector Machines ◽  
Different Sources ◽  
Document Level

This paper discusses an important issue in computational linguistics: classifying texts as formal or informal style. Our work describes a genre-independent methodology for building classifiers for formal and informal texts. We used machine learning techniques to do the automatic classification, and performed the classification experiments at both the document level and the sentence level. First, we studied the main characteristics of each style, in order to train a system that can distinguish between them. We then built two datasets: the first dataset represents general-domain documents of formal and informal style, and the second represents medical texts. We tested on the second dataset at the document level, to determine if our model is sufficiently general, and that it works on any type of text. The datasets are built by collecting documents for both styles from different sources. After collecting the data, we extracted features from each text. The features that we designed represent the main characteristics of both styles. Finally, we tested several classification algorithms, namely Decision Trees, Naïve Bayes, and Support Vector Machines, in order to choose the classifier that generates the best classification results.

scholarly journals Machine Learning Techniques for Heart Failure Prediction

2021 ◽  
Vol 6 (2) ◽  
pp. 872
Author(s):  
Nur Shahellin Mansur Huang ◽  
Zaidah Ibrahim ◽  
Norizan Mat Diah
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Heart Failure ◽  
Support Vector Machine ◽  
Public Health Problem ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Performance Score ◽  
Learning Techniques

This paper discusses the performance of four popular machine learning techniques for predicting heart failure using a publicly available dataset from kaggle.com, which are Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR).  They were selected due to their good performance in medical-related applications.  Heart failure is a common public health problem, and there is a need to improve the management of heart failure cases to increase the survival rate.  The vast amount of medical data related to heart failure and the availability of powerful computing devices allow researchers to conduct more experiments. The performance of the machine learning techniques was measured by accuracy, precision, recall, f1-score, sensitivity, and specificity in predicting heart failure with 13 symptoms or features. Experimental analysis showed that RF produces the highest performance score, which is 0.88 compared to SVM, NB, and LR.  Further experiments with RF were also conducted to determine the important features in predicting heart failure, and the results indicated that all 13 symptoms or features are important.

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