Artificial plant optimization algorithm to detect heart rate &amp; presence of heart disease using machine learning

Sharma, Prerna; Choudhary, Krishna; Gupta, Kshitij; Chawla, Rahul; Gupta, Deepak; Sharma, Arun

doi:10.1016/j.artmed.2019.101752

Cited by 60 publications

(27 citation statements)

References 17 publications

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“…Where most common datasets that are utilized such as UCI Heart Disease dataset, Cleveland dataset and Starlog heart disease datasets. In [24], [29], [30] and [33], they have utilized 10 k fold-validation for dataset splitting and validation. Only [23] have utilized 5 fold-cross validation in their experiment.…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

“…Gokulnath and Shantharajah [30] have introduced an optimizer based on the Support Vector Machine (SVM) classifier function. Genetic algorithm (GA) is one of the uses of this objective function.…”

Section: B Heart Diseasementioning

confidence: 99%

“…Regular health monitoring is required to prevent any unwanted circumstances [24]. Most common initial symptoms are deliberated such as fatigue and chest pain for an early assumption to cardiovascular disorder [30]. Lifestyle elements can dominate the risk of a patient.…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

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Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

Section: B Heart Diseasementioning

confidence: 99%

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Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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“…Machine Learning Algorithms (MLAs) have been applied in several domains to help augment human efforts to reduce some of the challenges posed by earlier technologies. Some of these domains include estimating soil cohesion and rainfall-induced soil erosion [9], [10], rainfall and flood forecasting [11]- [14], early heart disease and heart rate detection [15]- [18], financial projection and fraud detection [5], [6], [19], [20], defects in conductors and semiconductors manufacturing [21], and bike-rental estimation [22]. MLAs can be grouped as (i) supervised learning, (ii) semi-supervised learning, (iii) unsupervised learning, and (iv) reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Empirical Analysis of 21 Machine Learning Algorithms for Real-World Applications in Diverse Domains

Nti¹,

Aning²,

Ayawli³

et al. 2021

Preprint

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The application of computational methods like Machine Learning (ML) and Artificial Intelligence (AI) in several fields has recently received increased attention from academicians and field professionals. However, the availability of several of these computational techniques requires expertise to make the right choice. In making things easy for non-experts and inexperienced practitioners to make a suitable choice, some studies sought to compare the performance of different ML algorithms (MLAs) in solving problems in unique fields. However, most of the previous studies were domain centered on a handful of MLAs, which does not give a fairground for all-inclusive comparative analysis. Also, many of these studies compared MLAs for classification tasks to the best of our knowledge without considering the same MLAs performance for regression problems. This paper examines the performance of twenty-one (21) MLAs for classification and regression tasks based on six datasets from different domains. Empirically we compare their prediction results based on accuracy, balanced accuracy, F-score, Area Under the Curve (AUC), root mean square error, r-squared and adjusted r-squared. The random forest algorithm gave a consistent performance across all the six datasets in classification and regression tasks. However, on average, the XGBoost outperformed all MLAs applied in this study. The dummy algorithm and the linear regression were more moderate than the rest of the applied MLAs in computational complexity. Nevertheless, the overall study outcome shows that MLAs algorithms efficiently solve everyday challenges in elections outcome, financial fraud, network intrusion detection, meteorological forecast and heart diseases discovery; but their performance varies across domains and dataset dimensions.

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“…The application of AI and ML automation in health maintenance starts with the development of the first ML system called MYCIN [7] , which was programmed to suggest antibiotics for the treatment of bacterial infected patients by using a data of 450 rules collected from a team of medical experts. Subsequently, several ML techniques have been employed as promising technology against various contagious (SARS [8] , [9] , [10] , EBOLA [11] , HIV [12] , [13] ) and non-contagious (Cancer [14] , Diabetic [15] , Heart disease [16] , and Stroke [17] ) epidemic outbreaks. These aforementioned pieces of shreds of evidence encourage researchers to face down the current epidemic using effective approaches of ML and AI technologies.…”

Section: Introductionmentioning

confidence: 99%

A concise discussion on the potential spectral tools for the rapid COVID-19 detection

Mohanty

Fatrekar

Krishnan

et al. 2021

Results in Chemistry

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Developing robust methods to detect the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a causative agent for the current global health pandemic, is an exciting area of research. Nevertheless, the currently used conventional reverse transcription-polymerase chain reaction (RT-PCR) technique in COVID-19 diagnosis endures with some inevitable limitations. Consequently, the establishment of rapid diagnostic tools and quick isolation of infected patients is highly essential. Furthermore, the requirement of point-of-care testing is the need of the hour. Considering this, we have provided a brief review of the use of very recently reported robust spectral tools for rapid COVID-19 diagnosis. The spectral tools include, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), with the admittance of principal component analysis (PCA) and machine learning (ML) for meeting the high-throughput and fool-proof diagnostic platforms for the detection of SARS-CoV-2, are reviewed. Recently, these techniques have been readily applied to screen a large number of suspected patients within a short period and they demonstrated higher sensitivity for the detection of COVID-19 patients from unaffected human subjects.

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Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning

Cited by 60 publications

References 17 publications

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

A Comparative Empirical Analysis of 21 Machine Learning Algorithms for Real-World Applications in Diverse Domains

A concise discussion on the potential spectral tools for the rapid COVID-19 detection

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