Identifying the Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System

Haq, Amin Ul; Li, Minglu; Khan, Jalaluddin; Memon, Muhammad Hammad; Parveen, Shadma; Raji, Mordecai F.; Akbar, Wasif; Ahmad, Tanvir; Ullah, Sana; Shoista, Latipova; Monday, Happy Nkanta

doi:10.1109/iccwamtip47768.2019.9067519

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…For example, under the performance metrics, the majority of the articles recorded the performance of the algorithms used under the various performance metrics. [85], [88], [89], [95] 2 Solutions in Engineering 8 [14], [27], [62], [76], [86], [92], [110], [114] 3…”

Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…Experiments were performed by Amin and his team to categorize the performance of various feature selection algorithms. The experiments proved that Super Vector Machine classifier had excellent performance among other classifiers and achieved 86% classification accuracy [19].…”

Section: Introductionmentioning

confidence: 85%

Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

Abu-Alhaija¹,

Turab²

2022

Intelligent Automation &Amp; Soft Computing

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Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, such that clinicians can get alerted about patient's critical condition and act accordingly. Since the probability of false alarms pose serious impact to the accuracy of cardiac arrhythmia detection, it is the most important factor to keep false alarms to the lowest level. The proposed method in this paper demonstrates an example of how machine learning can contribute to health technologies with in detecting heart disease through minimizing negative false alarms. Stages of heartbeat learning model are proposed and explained besides the stages heartbeat anomalies detection stages.

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“…To evaluate our model, we calculated and compared the specificity, sensitivity and precision [11][12][13][14][15][16][17][18] of our model with that of experts' diagnosis over the test set.…”

Section: Methodsmentioning

confidence: 99%

Wavelet-Based Cough Signal Decomposition for Multimodal Classification

Agbley

Haq

et al. 2020

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

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Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency CepstralCoefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.

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Identifying the Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System

Cited by 19 publications

References 26 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

Wavelet-Based Cough Signal Decomposition for Multimodal Classification

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