Abstract
Supporting Institution
İNÖNÜ ÜNİVERSİTESİ BİYOİSTATİSTİK VE TIP BİLİŞİMİ ANABİLİM DALI
References
- [1] Hurwitz, J., et al., Augmented intelligence: the business power of human–machine collaboration. 2019: CRC Press.
- [2] Widmer, R.J., et al. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. in Mayo Clinic Proceedings. 2015. Elsevier.
- [3] Fan, C.-Y., et al., A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Applied Soft Computing, 2011. 11(1): p. 632-644.
- [4] Saygin, E. and M. Baykara, Karaciğer Yetmezliği Teşhisinde Özellik Seçimi Kullanarak Makine Öğrenmesi Yöntemlerinin Başarılarının Ölçülmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 2021. 33(2): p. 367-377.
- [5] Özekes, S., Veri madenciliği modelleri ve uygulama alanları. 2003.
- [6] Pujari, A.K., Data mining techniques. 2001: Universities press.
- [7] Chakraborty, K., et al., Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 2020. 97: p. 106754.
- [8] Akbulut, S., Veri madenciliği teknikleri ile bir kozmetik markanın ayrılan müşteri analizi ve müşteri segmentasyonu. Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2006.
- [9] Hosmer Jr, D.W., S. Lemeshow, and R.X. Sturdivant, Applied logistic regression. Vol. 398. 2013: John Wiley & Sons.
- [10] GUJARATİ, D.N., Temel Ekonometri (Çev. Ümit Senesen). Literatür Yayıncılık, İstanbul, 2005.
- [11] Macphee, C.H. and A. Daugherty, Cardiovascular diseases. Drug Discovery Today: Therapeutic Strategies, 2008. 1(5): p. 1-3.
- [12] Patel, J., D. TejalUpadhyay, and S. Patel, Heart disease prediction using machine learning and data mining technique. Heart Disease, 2015. 7(1): p. 129-137.
- [13] Feyyad, U., Data mining and knowledge discovery: Making sense out of data. IEEE expert, 1996. 11(5): p. 20-25.
- [14] Kumar, A.S. and R. Wahidabanu, Data Mining Association Rules for Making Knowledgeable Decisions, in Data Mining Applications for Empowering Knowledge Societies. 2009, IGI Global. p. 43-53.
- [15] Bahrami, B. and M.H. Shirvani, Prediction and diagnosis of heart disease by data mining techniques. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2015. 2(2): p. 164-168.
- [16] YILMAZ, R. and F.H. YAĞIN, Early detection of coronary heart disease based on machine learning methods. Medical Records, 2022. 4(1): p. 1-6.
Abstract
Aim: In addition to affecting the individual sociologically and psychologically, heart disease also poses important problems in health systems. Evaluation of heart disease performances has gained great importance in terms of machine learning method. In the study, performances were compared with the machine learning method for risk methods that classify heart illness.
Materials and Methods: The categorization process Throughout the research made use of the "Heart Disease Dataset," an open access dataset. F1-score, sensitivity, selectivity, accuracy, balanced accuracy, negative and positive predictive values were used to assess the performance of the categorisation model using the machine learning approach. Random forest method, one of the variable selection methods, was used.
Results: According to the relational classification model's classification findings for heart disease, the accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score values were observed to be 0.997, 0.997, 0.995, 1, 1, and 0.995, respectively.
Conclusion: The relational classification model proposed in the analysis obtained in the web-based open access dataset yielded distinctively successful results in classifying heart disease according to performance criteria.
References
- [1] Hurwitz, J., et al., Augmented intelligence: the business power of human–machine collaboration. 2019: CRC Press.
- [2] Widmer, R.J., et al. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. in Mayo Clinic Proceedings. 2015. Elsevier.
- [3] Fan, C.-Y., et al., A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Applied Soft Computing, 2011. 11(1): p. 632-644.
- [4] Saygin, E. and M. Baykara, Karaciğer Yetmezliği Teşhisinde Özellik Seçimi Kullanarak Makine Öğrenmesi Yöntemlerinin Başarılarının Ölçülmesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 2021. 33(2): p. 367-377.
- [5] Özekes, S., Veri madenciliği modelleri ve uygulama alanları. 2003.
- [6] Pujari, A.K., Data mining techniques. 2001: Universities press.
- [7] Chakraborty, K., et al., Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 2020. 97: p. 106754.
- [8] Akbulut, S., Veri madenciliği teknikleri ile bir kozmetik markanın ayrılan müşteri analizi ve müşteri segmentasyonu. Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2006.
- [9] Hosmer Jr, D.W., S. Lemeshow, and R.X. Sturdivant, Applied logistic regression. Vol. 398. 2013: John Wiley & Sons.
- [10] GUJARATİ, D.N., Temel Ekonometri (Çev. Ümit Senesen). Literatür Yayıncılık, İstanbul, 2005.
- [11] Macphee, C.H. and A. Daugherty, Cardiovascular diseases. Drug Discovery Today: Therapeutic Strategies, 2008. 1(5): p. 1-3.
- [12] Patel, J., D. TejalUpadhyay, and S. Patel, Heart disease prediction using machine learning and data mining technique. Heart Disease, 2015. 7(1): p. 129-137.
- [13] Feyyad, U., Data mining and knowledge discovery: Making sense out of data. IEEE expert, 1996. 11(5): p. 20-25.
- [14] Kumar, A.S. and R. Wahidabanu, Data Mining Association Rules for Making Knowledgeable Decisions, in Data Mining Applications for Empowering Knowledge Societies. 2009, IGI Global. p. 43-53.
- [15] Bahrami, B. and M.H. Shirvani, Prediction and diagnosis of heart disease by data mining techniques. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2015. 2(2): p. 164-168.
- [16] YILMAZ, R. and F.H. YAĞIN, Early detection of coronary heart disease based on machine learning methods. Medical Records, 2022. 4(1): p. 1-6.
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | July 2, 2023 |
| Publication Date | July 1, 2023 |
| Published in Issue | Year 2023 Volume: 8 Issue: 1 - JCS_Vol_8_No_1_2023 |
