scholarly journals Investigation of Classifiers Algorithms of ML for Heart Disease Prediction

2021 ◽  
Vol 9 (VI) ◽  
pp. 3025-3033
Author(s):  
Ved Prakash Singh
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Data Analysis ◽  
Movement Disorders ◽  
Learning Technologies ◽  
The Body ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Key Factor ◽  
Ultimate Objective

A ML computer plays an important role in predicting the presence or absence of movement disorders and heart disease. The resting part of the body as compared to the Heart s, is the largest and most concentrated organ in the human body. Data analysis helps in predicting heart disease in the medical field is an important task. Machine learning is recycled in the medical industry throughout the world. The presence or absence of movement disorders and cardiac diseases is a key factor in machine learning. Data analysis helps predict more information and prevents various diseases in medical centers. The main impartial of the research paper is toward predict a patient cardiac disease using an algorithm for machine learning as a random forest is most predictable. A large number of patient data are kept every month. The data stored can be used to predict future diseases. Certain data mining and machine learning technologies are used to forecast heart disease, including artificial neural networks (ANN), decision trees, fuzzy logic, K-Nearest neighbors (KNN), naive bays and vector supporting equipment (SVM). The ultimate objective of this paper is to inspect the best logistic regression which signifies the machine's python learning. The UCI machine learning depot used the data sets of heart disease.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

scholarly journals Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Marcos Fabietti ◽  
Mufti Mahmud ◽  
Ahmad Lotfi
Keyword(s):  
Machine Learning ◽  
Open Access ◽  
Short Term Memory ◽  
The Body ◽  
Data Sets ◽  
Additional Information ◽  
Machine Learning Model ◽  
Signal Characteristics ◽  
Wide Range ◽  
Memory Network

AbstractAcquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.

scholarly journals Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

2019 ◽  
Vol 11 (14) ◽  
pp. 1714
Author(s):  
Eija Honkavaara ◽  
Konstantinos Karantzalos ◽  
Xinlian Liang ◽  
Erica Nocerino ◽  
Ilkka Pölönen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Data Analysis ◽  
Spectral Imaging ◽  
Learning Technologies ◽  
Special Issue ◽  
Multimodal Data

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.

scholarly journals An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

2021 ◽  
Author(s):  
Diti Roy ◽  
Md. Ashiq Mahmood ◽  
Tamal Joyti Roy
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Modern Technology ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Day By Day

<p>Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.<i><b></b></i></p> <p><b> </b></p>

scholarly journals Machine learning in diachronic corpus phonology: mining verse data to infer trajectories in English phonotactics

2018 ◽  
Vol 3 ◽  
Author(s):  
Andreas Baumann
Keyword(s):  
Machine Learning ◽  
Middle English ◽  
Large Data ◽  
Large Data Sets ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Powerful Method ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Standard Techniques

Machine learning is a powerful method when working with large data sets such as diachronic corpora. However, as opposed to standard techniques from inferential statistics like regression modeling, machine learning is less commonly used among phonological corpus linguists. This paper discusses three different machine learning techniques (K nearest neighbors classifiers; Naïve Bayes classifiers; artificial neural networks) and how they can be applied to diachronic corpus data to address specific phonological questions. To illustrate the methodology, I investigate Middle English schwa deletion and when and how it potentially triggered reduction of final /mb/ clusters in English.

scholarly journals Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Kardiologiia ◽  
2020 ◽  
Vol 60 (10) ◽  
pp. 38-46
Author(s):  
B. I. Geltser ◽  
K. J. Shahgeldyan ◽  
V. Y. Rublev ◽  
V. N. Kotelnikov ◽  
A. B. Krieger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Clinical Laboratory ◽  
Artery Bypass ◽  
Fatal Outcome ◽  
Left Ventricular ◽  
Learning Technologies ◽  
Relative Mass ◽  
Learning Methods ◽  
Machine Learning Methods

Aim      To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.Material and methods  A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008–2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models.Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF) <30 %, EF 30-50 %, age of patients with recent MI, damage of peripheral arterial circulation, urgency of CB, functional class III-IV chronic heart failure, and 5 additional predictors, including heart rate, systolic blood pressure, presence of aortic stenosis, posterior left ventricular (LV) wall relative thickness index (RTI), and LV relative mass index (LVRMI). The models developed by the authors using LR, RF and ANN methods had higher AUC values and sensitivity compared to the classical EuroSCORE II scale. The ANN models including the RTI and LVRMI predictors demonstrated a maximum level of prognostic accuracy, which was illustrated by values of the quality metrics, AUC 93 %, sensitivity 90 %, and specificity 96 %. The predictive robustness of the models was confirmed by results of the control validation.Conclusion      The use of current machine-learning technologies allowed developing a novel algorithm for selection of predictors and highly accurate models for predicting an in-hospital fatal outcome after CB. 

scholarly journals Human Activity Recognition of Exoskeleton Robot with Supervised Learning Techniques

2021 ◽  
Author(s):  
Jiacheng Mai ◽  
zhiyuan chen ◽  
Chunzhi Yi ◽  
Zhen Ding
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
The Body ◽  
Lower Limbs ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Method

Abstract Lower limbs exoskeleton robots improve the motor ability of humans and can facilitate superior rehabilitative training. By training large datasets, many of the currently available mobile and signal devices that may be worn on the body can employ machine learning approaches to forecast and classify people's movement characteristics. This approach could help exoskeleton robots improve their ability to predict human activities. Two popular data sets are PAMAP2, which was obtained by measuring people's movement through inertial sensors, and WISDM, which was collected people's activity information through mobile phones. With the focus on human activity recognition, this paper applied the traditional machine learning method and deep learning method to train and test these datasets, whereby it was found that the prediction performance of a decision tree model was highest on these two data sets, which is 99% and 72% separately, and the time consumption of decision tree is the least. In addition, a comparison of the signals collected from different parts of the human body showed that the signals deriving from the hands presented the best performance in terms of recognizing human movement types.

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