Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning
The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the su...
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MDPI AG
2021-10-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6584 |
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| author | Deepak Kumar Chaman Verma Sanjay Dahiya Pradeep Kumar Singh Maria Simona Raboaca Zoltán Illés Brijesh Bakariya |
| author_facet | Deepak Kumar Chaman Verma Sanjay Dahiya Pradeep Kumar Singh Maria Simona Raboaca Zoltán Illés Brijesh Bakariya |
| author_sort | Deepak Kumar |
| collection | DOAJ |
| description | The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today’s intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (<i>p</i> < 0.05). Anemia, diabetes, and BP features had no significant impact on the SS and SC level in the patient (<i>p</i> > 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient’s survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient’s age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient’s survival in the situation. |
| first_indexed | 2024-03-10T06:51:03Z |
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| language | English |
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| publishDate | 2021-10-01 |
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| spelling | doaj.art-555ca905f3cf4481adf167c2f23f70ce2023-11-22T16:48:19ZengMDPI AGSensors1424-82202021-10-012119658410.3390/s21196584Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine LearningDeepak Kumar0Chaman Verma1Sanjay Dahiya2Pradeep Kumar Singh3Maria Simona Raboaca4Zoltán Illés5Brijesh Bakariya6Apex Institute of Technology, Chandigarh University, Mohali 140413, Punjab, IndiaDepartment of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, HungaryDepartment of Computer Science and Engineering, Ch. Devi Lal State Institute of Engineering & Technology, Sirsa 125077, Haryana, IndiaDepartment of Computer Science, KIET Group of Institutions, Ghaziabad 201206, Uttar Pradesh, IndiaICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, RomaniaDepartment of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, HungaryDepartment of Computer Application, I.K. Gujral Punjab Technical University, Jalandhar 144603, Punjab, IndiaThe incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today’s intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (<i>p</i> < 0.05). Anemia, diabetes, and BP features had no significant impact on the SS and SC level in the patient (<i>p</i> > 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient’s survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient’s age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient’s survival in the situation.https://www.mdpi.com/1424-8220/21/19/6584cardiac diseasefeature selectionmulticollinearitymachine learningIoT |
| spellingShingle | Deepak Kumar Chaman Verma Sanjay Dahiya Pradeep Kumar Singh Maria Simona Raboaca Zoltán Illés Brijesh Bakariya Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning Sensors cardiac disease feature selection multicollinearity machine learning IoT |
| title | Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning |
| title_full | Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning |
| title_fullStr | Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning |
| title_full_unstemmed | Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning |
| title_short | Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning |
| title_sort | cardiac diagnostic feature and demographic identification cdf di an iot enabled healthcare framework using machine learning |
| topic | cardiac disease feature selection multicollinearity machine learning IoT |
| url | https://www.mdpi.com/1424-8220/21/19/6584 |
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