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The massive variation in the economic growth and development of countries worldwide, accompanied by the irregular availability of medical experts and radiologists, is a major impediment to early diagnosis. Researchers are working on prediction systems that will aid doctors and radiologists in prognostication and assessment by providing diagnostics to the human race without regard to geographical, economic, or financial inequalities. The use of a computational intelligence-based medical imaging prediction system to either prognosticate or detect and further diagnose the disease is becoming more popular. In this work, a computational intelligence-based prediction system,\n                    <jats:italic toggle=\"yes\">PHtNN<\/jats:italic>\n                    , for heart disease diagnosis has been proposed.\n                    <jats:italic toggle=\"yes\">PHtNN<\/jats:italic>\n                    uses the Multiple Factor Analysis (MFA) to extract features from the heart disease multi-datasets, VA Long Beach, Switzerland, Hungarian, Cleveland, and Z-Alizadeh Sani, and train the model by using twin neural network. The system,\n                    <jats:italic toggle=\"yes\">PHtNN<\/jats:italic>\n                    , is validated using the hold-out validation scheme with a ratio of 3:1. Experimental results reveal that\n                    <jats:italic toggle=\"yes\">PHtNN<\/jats:italic>\n                    outperforms several previous baseline approaches in terms of accuracy and improves the system\u2019s efficiency; as a result, it can assist medical experts in diagnosing cardiac patients.\n                  <\/jats:p>","DOI":"10.1145\/3803787","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T13:47:29Z","timestamp":1775569649000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<i>PHtNN<\/i>\n                    : Prediction of Heart Disease Risk Using Twin Neural\u00a0Network"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6515-2596","authenticated-orcid":false,"given":"Ankur","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India and Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9266-9515","authenticated-orcid":false,"given":"Rahul","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Rajiv Gandhi Institute of Petroleum Technology, Jais, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6277-6267","authenticated-orcid":false,"given":"Balasubramanian","family":"Raman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6866-8814","authenticated-orcid":false,"given":"Harkirat Singh","family":"Arora","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"WHO. 2019. 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