{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:37:30Z","timestamp":1792082250201,"version":"4.4.0"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew\u2019s correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.<\/jats:p>","DOI":"10.3390\/diagnostics14070736","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:44:52Z","timestamp":1711892692000},"page":"736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["An Integrated Machine Learning Approach for Congestive Heart Failure Prediction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9196-968X","authenticated-orcid":false,"given":"M. Sheetal","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology Manipur, Langol, Imphal 795004, Manipur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khelchandra","family":"Thongam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology Manipur, Langol, Imphal 795004, Manipur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4337-7273","authenticated-orcid":false,"given":"Prakash","family":"Choudhary","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Central University of Rajasthan, Tehsil Kishangarh, Ajmer 305817, Rajasthan, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. K.","family":"Bhagat","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"ref_1","unstructured":"Zipes, D., Libby, P., Bonow, R., and Braunwald, E. (2004). Heart Disease: A Textbook of Cardiovascular Medicine, Elsevier Science. [7th ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ejcnurse.2006.06.001","article-title":"Predictors for health related quality of life in persons 65 years or older with chronic heart failure","volume":"6","author":"Saveman","year":"2007","journal-title":"Eur. J. Cardiovasc. 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