Perinatal Hypoxia Diagnostic System by using Scalable Machine Learning Algorithms
Harmandeep Kaur1, Vikas Khullar2, Harjit Pal Singh3, Manju Bala4
1Harmandeep Kaur, CSE, CT Institute of Engineering Management and Technology, Jalandhar, India.
2Vikas Khullar, CSE, CT Institute of Engineering Management and Technology, Jalandhar, India.
3Harjit Pal Singh, ECE, CT Institute of Engineering Management and Technology, Jalandhar, India.
4Manju Bala, CSE, Khalsa College of Engineering and Technology, Amritsar, India.
Manuscript received on September 15, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1954-1959 | Volume-8 Issue-12, October 2019. | Retrieval Number: L29051081219/2019©BEIESP | DOI: 10.35940/ijitee.L2905.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Collaborating big data and machine learning approaches in healthcare can help in improving clinical decision making and treatment by identifying and accumulating accurate features. Prenatal hypoxia can also be identified by cardiotocography (CTG) monitoring that helps in identifying the condition of the fetus. Imposing the data over distributed approaches can help in fast computation to rate the fetal and mother wellbeing before delivery. Our research aims to propose and implement a scalable Machine learning Algorithm based perinatal Hypoxia diagnostic system for larger datasets. This system was implemented on the CTG dataset using python and pyspark models like SVM, Random Forest, and Logistic regression. In the proposed method experiment results contributing to spark RF are more accurate than other techniques and achieved the precision of 0.97, recall of 0.99, f-1 score of 0. 98, AUC of 0.97 and gained 97% accuracy.
Keywords: Prenatal Hypoxia, CTG, Pyspark, fetal State, and Machine Learning Techniques.
Scope of the Article: Machine Learning