Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis

Ismaeel, Salam; Miri, Ali; Chourishi, Dharmendra

doi:10.1109/ihtc.2015.7238043

Cited by 71 publications

(21 citation statements)

References 15 publications

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“…The output indicated the high accuracy measured due to feature selection approach [7]. Furthermore, the Extreme Learning Machine techniques using feedforward neural network applied on Cleveland data based on 300 patients, suggested 80% accuracy in forecasting the heart disease in a patient [8]. In another research, the neural network applied using multi-layer perceptron, which also known as supervised learning.…”

Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 95%

“…Noticeably, these reasons are independent from each other; proper analysis on this kind of dataset can improve the process of diagnosing and can assist the heart surgeons as well. Previously, different researches used number of techniques to improve the HF diagnosis process such as Extreme Learning Machine [8], heart disease classification [9], and machine learning classifiers [1].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

175

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In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon's point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

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Section: Machine Learning Classifiers For Heart Disease Predictionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

175

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“…Confer to the new study [14] by Kadi et al has completed a pragmatic research after hands-on 149 papers proclaimed during the period from 2000-2015 for the prognosis of CVDs, DT, SVM and ANN were established to be the most periodically used ML techniques. An extreme machine learning (EML) were also implemented to predict heart disease (HD) by using UCI datasets repository and achieved highest accuracy of 80% [15]. GA and fuzzy logic (Hybrid genetic Fuzzy) approach trained and certified over similar UCI repository dataset with maximum accuracy of 86% [16].…”

Section: Review Of Relevant Workmentioning

confidence: 99%

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Javid¹,

Alsaedi²,

Ghazali³

2020

IJACSA

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To solve many problems in data science, Machine Learning (ML) techniques implicates artificial intelligence which are commonly used. The major utilization of ML is to predict the conclusion established on the extant data. Using an established dataset machine determine emulate and spread them to an unfamiliar data sets to anticipate the conclusion. A few classification algorithm's accuracy prediction is satisfactory, although other perform limited accuracy. Different ML and Deep Learning (DL) networks established on ANN have been extensively recommended for the disclosure of heart disease in antecedent researches. In this paper, we used UCI Heart Disease dataset to test ML techniques along with conventional methods (i.e. random forest, support vector machine, K-nearest neighbor), as well as deep learning models (i.e. long short-term-memory and gated-recurrent unit neural networks). To improve the accuracy of weak algorithms we explore voting based model by combining multiple classifiers. A provisional cogent approach was used to regulate how the ensemble technique can be enforced to improve an accuracy in the heart disease prediction. The strength of the proposed ensemble approach such as voting based model is compelling in improving the prognosis accuracy of anemic classifiers and established adequate achievement in analyze risk of heart disease. A superlative increase of 2.1% accuracy for anemic classifiers was attained with the help of an ensemble voting based model.

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“…In our approach, we use the extreme learning machine (ELM) [3,21] for intrusion detection. The ELM algorithm has been used in a diverse set of applications including water quality forecasting [22], optimization of industrial chemical productions [23], big data processing [24], speech enhancement [25], heart disease diagnosis [26], medical image segmentation [27], and fault detection [28,29].…”

Section: Related Workmentioning

confidence: 99%

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

Sharma

Giri

Granmo

et al. 2019

EURASIP J. on Info. Security

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Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we divide the multi-class problem into multiple binary classifications. We test our method on the UNSW and KDDcup99 datasets. The results clearly show that our proposed method is able to outperform all the other methods, with a high margin. Our system is able to achieve 98.24% and 99.76% accuracy for multi-class classification on the UNSW and KDDcup99 datasets, respectively. Additionally, we use the weighted extreme learning machine to alleviate the problem of imbalance in classification of attacks, which further boosts performance. Lastly, we implement the ensemble of ELMs in parallel using GPUs to perform intrusion detection in real time.

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Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis

Cited by 71 publications

References 15 publications

Implementation of Machine Learning Model to Predict Heart Failure Disease

Implementation of Machine Learning Model to Predict Heart Failure Disease

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation

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