{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:06:17Z","timestamp":1764842777684,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSPD2023R890"],"award-info":[{"award-number":["RSPD2023R890"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.<\/jats:p>","DOI":"10.3390\/s23187756","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T08:01:30Z","timestamp":1694160090000},"page":"7756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Diagnosis of Anterior and Inferior Myocardial Infarctions Using UWB Radar and AI-Driven Feature Fusion Approach"],"prefix":"10.3390","volume":"23","author":[{"given":"Kainat","family":"Zafar","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-2060","authenticated-orcid":false,"given":"Hafeez Ur Rehman","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan"}]},{"given":"Abdul","family":"Majid","sequence":"additional","affiliation":[{"name":"Cardiology Department, Sheikh Zayed Medical College & Hospital, Rahim Yar Khan 64200, Punjab, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[{"name":"School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1268-9613","authenticated-orcid":false,"given":"Sultan","family":"Alfarhood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7445-7121","authenticated-orcid":false,"given":"Mejdl","family":"Safran","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Mahmud, M.P., Saha, P.K., Gupta, K.D., and Siddique, Z. (2021). Effect of data scaling methods on machine learning algorithms and model performance. 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