{"doi":"10.1155/2022/8571970","title":"Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images","abstract":"<jats:p>The level of patient’s illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.</jats:p>","journal":"Computational and Mathematical Methods in Medicine","year":2022,"id":686487,"datarank":0.5050943744979712,"base_score":3.367295829986474,"endowment":3.367295829986474,"self_citation_contribution":0.5050943744979712,"citation_network_contribution":0.0,"self_endowment_contribution":0.5050943744979712,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":28,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1793453,"name":"V. E. Sathishkumar","orcid":"0000-0002-8271-2022","position":1,"is_corresponding":false},{"id":1793454,"name":"M. Sandeep Kumar","orcid":null,"position":2,"is_corresponding":false},{"id":1793455,"name":"V. Maheshwari","orcid":null,"position":3,"is_corresponding":false},{"id":1793456,"name":"J. Prabhu","orcid":"0000-0003-3335-6911","position":4,"is_corresponding":false},{"id":1793457,"name":"Shaikh Muhammad Allayear","orcid":"0000-0003-0567-7865","position":5,"is_corresponding":false},{"id":1793452,"name":"Kogilavani Shanmugavadivel","orcid":"0000-0002-0715-143X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images","abstract":"<jats:p>The level of patient’s illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.</jats:p>","is_dataset_classified":null,"base_score":3.367295829986474,"endowment":3.367295829986474,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"36132548","pmcid":"PMC9484938","openalex_id":"https://openalex.org/W4295309597","authors":[],"funders":[],"total_grants":0,"fwci":3.8365,"citation_percentile":0.94854101,"influential_citations":0,"citation_trend":[{"year":2023,"count":13},{"year":2024,"count":6},{"year":2025,"count":6},{"year":2026,"count":3}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://downloads.hindawi.com/journals/cmmm/2022/8571970.pdf","host_type":"journal"},{"url":"https://downloads.hindawi.com/journals/cmmm/2022/8571970.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/cmmm/2022/8571970.pdf","host_type":"publisher"},{"url":"http://downloads.hindawi.com/journals/cmmm/2022/8571970.xml","host_type":"publisher"},{"url":"https://doi.org/10.1155/2022/8571970","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/36132548","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9484938","host_type":"repository"},{"url":"https://doaj.org/article/acff56b188904de2b366ca4d2f15e368","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC9484938","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC9484938?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["ECG Monitoring and Analysis","EEG and Brain-Computer Interfaces","Phonocardiography and Auscultation Techniques","Algorithms","Arrhythmias, Cardiac","Bayes Theorem","Deep Learning","Electrocardiography","Humans","Machine Learning","Support Vector Machine"],"mesh_terms":["Machine Learning","Deep Learning","Algorithms","Arrhythmias, Cardiac","Bayes Theorem","Electrocardiography","Humans","Support Vector Machine"],"keywords":["Hyperparameter","Artificial intelligence","Machine learning","Hyperparameter optimization","Computer science","Support vector machine","Deep learning","Convolutional neural network","Naive Bayes classifier","Logistic regression","Cardiac arrhythmia","Artificial neural network"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-18T19:38:32.170332Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}