Fetal health classification from cardiotocographic data using machine learning
Abolfazl Mehbodniya
Department of Electronics and Communications Engineering, Kuwait College of Science and Technology, Kuwait, Kuwait
Search for more papers by this authorArokia Jesu Prabhu Lazar
Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India
Search for more papers by this authorDilip Kumar Sharma
Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
Search for more papers by this authorSanthosh Jayagopalan
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
Search for more papers by this authorKousalya K
Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamil Nadu, India
Search for more papers by this authorPallavi Singh
School of Allied Health Sciences, Jaipur National University, Jaipur, Rajasthan, India
Search for more papers by this authorRegin Rajan
Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
Search for more papers by this authorSharnil Pandya
Department of CSIT and AIML, Symbiosis International University, Pune, Maharashtra, India
Search for more papers by this authorCorresponding Author
Sudhakar Sengan
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
Correspondence
Sudhakar Sengan, Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli–627152, Tamil Nadu, India.
Email: [email protected]
Search for more papers by this authorAbolfazl Mehbodniya
Department of Electronics and Communications Engineering, Kuwait College of Science and Technology, Kuwait, Kuwait
Search for more papers by this authorArokia Jesu Prabhu Lazar
Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India
Search for more papers by this authorDilip Kumar Sharma
Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
Search for more papers by this authorSanthosh Jayagopalan
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
Search for more papers by this authorKousalya K
Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamil Nadu, India
Search for more papers by this authorPallavi Singh
School of Allied Health Sciences, Jaipur National University, Jaipur, Rajasthan, India
Search for more papers by this authorRegin Rajan
Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
Search for more papers by this authorSharnil Pandya
Department of CSIT and AIML, Symbiosis International University, Pune, Maharashtra, India
Search for more papers by this authorCorresponding Author
Sudhakar Sengan
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
Correspondence
Sudhakar Sengan, Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli–627152, Tamil Nadu, India.
Email: [email protected]
Search for more papers by this authorAbstract
Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi-layer perceptron, and K-nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1-score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.
CONFLICT OF INTEREST
The authors show no conflict of interest to submit this paper to this journal.
Open Research
DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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