Support vector machines in neonatal mortality detection: a comprehensive scoping review with disease-specific emphasis




Support vector machines, Neonatal mortality, Disease-specific emphasis


Neonatal mortality is a widely significant problem since diseases such as sepsis, apnea and jaundice have claimed the lives of 2.3 million neonates in 2021. As such, better tools need to be developed to reduce its rate. While traditional methods like Clinical risk index (CRIB) and Score for neonatal acute physiology (SNAP) have proven helpful in predicting neonatal mortality, there is a need for more efficient measures. One such approach is support vector machine (SVM), a supervised machine-learning algorithm that is primarily used for classification. SVM can perform both linear and non-linear classification; it conducts the latter with the assistance of the kernel trick and functions such as polynomial, gaussian, RBF and sigmoid functions. This narrative review aims to explore the potential and limitations of SVM in predicting major global causes of neonatal mortality. We searched through articles employing SVM to predict different diseases and symptoms such as sepsis, seizures, fetal heart rate, low birth weight, hypoxic-ischemic encephalopathy, apnea, jaundice and neonatal respiratory distress syndrome, and concluded that while SVM has its merits and has shown promising results in many aspects, it also has its demerits such as requiring an extensive training time to achieve higher accuracy and precision.


Metrics Loading ...


Bhavsar H, Ganatra A. A Comparative Study of Training Algorithms for Supervised Machine Learning. Int J Soft Comp Eng. 2012;2.

UNICEF. Neonatal Mortality, 2023. Available at: Accessed on 04 August 2023.

Mfateneza E, Rutayisire PC, Biracyaza E, Musafiri S, Mpabuka WG. Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset. BMC Pregnancy Childbirth. 2022;22(1):388.

Muñoz Lezcano S, López F, Corbi A. Computer aided prediction of sepsis-related mortality risk in neonatal intensive care units. 2022.

Noble WS. What is a support vector machine? Nature Biotech. 2006;24(12):1565-7.

Baethge C, Goldbeck-Wood S, Mertens S. SANRA-a scale for the quality assessment of narrative review articles. Res Integr Peer Rev. 2019;4:5.

Nasteski V. An overview of the supervised machine learning methods. Horizons. 2017;4:51-62.

Pisner DA, Schnyer DM. Support vector machine. In: Mechelli A, Vieira S, eds. Machine Learning: Academic Press; 2020: 101-121.

Awad M, Khanna R. Support Vector Machines for Classification. In: Awad M, Khanna R, eds. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Berkeley, CA: Apress; 2015: 66.

Brereton RG, Lloyd GR. Support vector machines for classification and regression. Analyst. 2010;135(2):230-67.

Evgeniou T, Pontil M. Support Vector Machines: Theory and Applications. In: Paliouras G, Karkaletsis V, Spyropoulos CD, eds. Machine Learning and Its Applications: Advanced Lectures. Berlin, Heidelberg: Springer Berlin Heidelberg; 2001: 249-57.

Suthaharan S. Support Vector Machine. In: Suthaharan S, eds. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning. Boston, MA: Springer US; 2016: 207-35.

Saxena S. Beginner’s Guide to Support Vector Machine (SVM) Analytics Vidhya Analytics Vidhya, 2021. Available at: Accessed on 04 August 2023.

Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing. 2020;408:189-215.

Stoean R, Stoean C. Modeling Medical Decision Making by Support Vector Machines, Explaining by Rules of Evolutionary Algorithms with Feature Selection. Expert Systems with Applications. 2013;40:2677-86.

Jegan C. Classification Of Diabetes Disease Using Support Vector Machine. Int J Eng Res App. 2013;3:1797-801.

Yu JS, Xue AY, Redei EE, Bagheri N. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry. 2016;6(10):e931.

Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36(4):1140-52.

Zhang Y, Liu F, Zhao Z, Li D, Zhou X, Wang J. Studies on Application of Support Vector Machine in Diagnose of Coronary Heart Disease. Electromag Field Prob App; 2012.

Nugroho KS, Sukmadewa AY, Vidianto A, Mahmudy WF. Effective predictive modelling for coronary artery diseases using support vector machine. IAES Int J Artif Intell . 2022;11 (1):345-55.

Garg B, Sharma D, Farahbakhsh N. Assessment of sickness severity of illness in neonates: review of various neonatal illness scoring systems. J Matern Fetal Neonatal Med. 2018;31(10):1373-80.

Maiya PP, Nagashree S, Shaik MS. Role of score for neonatal acute physiology (SNAP) in predicting neonatal mortality. Indian J Pediatr. 2001;68(9):829-34.

Stomnaroska O, Danilovski D. The CRIB II (Clinical Risk Index for Babies II) Score in Prediction of Neonatal Mortality. Pril (Makedon Akad Nauk Umet Odd Med Nauki). 2020;41(3):59-64.

Rathod D, Adhisivam B, Bhat BV. Sick Neonate Score--A Simple Clinical Score for Predicting Mortality of Sick Neonates in Resource Restricted Settings. Indian J Pediatr. 2016;83(2):103-6.

Boghossian NS, Page GP, Bell EF, Stoll BJ, Murray JC, Cotten CM, et al. Late-onset sepsis in very low birth weight infants from singleton and multiple-gestation births. J Pediatr. 2013;162(6):1120-4.

Struzek C, Goldfarb DM, Schlattmann P, Schlapbach LJ, Reinhart K, Kissoon N. The global burden of paediatric and neonatal sepsis: a systematic review. Lancet Respir Med. 2018;6(3):223-30.

Hsu JF, Chang YF, Cheng HJ, Yang C, Lin CY, Chu SM, et al. Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit. J Pers Med. 2021;11(8):695.

Berg AT, Jallon P, Preux PM. The epidemiology of seizure disorders in infancy and childhood: definitions and classifications. In: Dulac O, Lassonde M, Sarnat HB, eds. Handbook of Clinical Neurology. Netherlands: Elsevier; 2013: 391-398.

Hsu JF, Chang YF, Cheng HJ, Yang C, Lin CY, Chu SM, Huang HR, Chiang MC, Wang HC, Tsai MH. Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit. J Pers Med. 2021;11(8):695.

Temko A, Thomas E, Marnane W, Lightbody G, Boylan G. EEG-based neonatal seizure detection with Support Vector Machines. Clin Neurophysiol. 2011;122(3):464-73.

Elakkiya R. Machine learning based intelligent automated neonatal epileptic seizure detection. J Intell Fuzzy Sys. 2021;40:8847-55.

Ansari AH, Cherian PJ, Dereymaeker A, Matic V, Jansen K, De Wispelaere L, et al. Clin Neurophysiol. 2016;127(9):3014-24.

Allen KA, Brandon DH. Hypoxic Ischemic Encephalopathy: Pathophysiology and Experimental Treatments. Newborn Infant Nurs Rev. 2011;11(3):125-33.

Cerebral Plasy Guide. Hypoxic ischemic encephalopathy: Cerebral Palsy Guide: Cerebral Palsy Guide, 2023. Available at: Accessed on 04 August 2023.

Ahmed R, Temko A, Marnane W, Lightbody G, Boylan G. Grading hypoxic-ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine. Clin Neurophysiol. 2016;127(1):297-309.

Doyle OM, Temko A, Murray DM, Lightbody G, Marnane W, Boylan GB. Predicting the neurodevelopmental outcome in newborns with hypoxic-ischaemic injury. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1370-3.

Kondamudi NP, Krata L, Wilt AS. Infant Apnea. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2023.

Vahabi N, Yerworth R, Miedema M, Kaam A, Bayford R, Demosthenous A. Deep Analysis of EIT Dataset to Classify Apnea and Non-Apnea Cases in Neonatal Patients. IEEE. 2021.

Shirwaikar R, Acharya D, Makkithaya K, Surulivelr M, Lewis L. Machine Learning Techniques for Neonatal Apnea Prediction. J Artif Intell. 2016;9:33-8.

WHED. Stanisława Staszica W K, European Society of C, Medicine IEi, 2012. Available at: Accessed on 04 August 2023.

Mishra S, Agarwal R, Deorari AK, Paul VK. Jaundice in the newborns. Indian J Pediatr. 2008;75(2):157-63.

Althnian A, Almanea N, Aloboud N. Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning. Sensors (Basel). 2021;21(21):7038.

Jafari T, Nasiri S, Sayadi M, Emami H, Mohammadpour S. A Neonatal jaundice prediction system based on the support vector machine algorithm. JHA. 2023;25(4):28-44.

NHS. Newborn respiratory distress syndrome: NHS UK, 2021. Available at: Accessed on 04 August 2023.

Lin W, Ruan J, Liu Z, Liu C, Wang J, Chen L, et al. Exploring the Diagnostic Value of Ultrasound Radiomics for Neonatal Respiratory Distress Syndrome. Res Square. 2023.

Khalilzad Z, Hasasneh A, Tadj C. Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features. Diagnostics (Basel). 2022;12(11):2802.

Walter JH. Metabolic acidosis in newborn infants. Arch Dis Child. 1992;67(7):767-9.

Lunghi F, Magenes G, Pedrinazzi L, Signorini MG. Detection of fetal distress though a support vector machine based on fetal heart rate parameters. Comp Cardiol. 2005; 2005.

Georgoulas G, Stylios C, Groumpos P. Classification of fetal heart rate using scale dependent features and support vector machines. IFAC Proceedings Volumes. 2005;38(1):313-8.

Smith J, Murphy M, Kandasamy Y. The IUGR infant: A case study and associated problems with IUGR infants. J Neonatal Nurs. 2013;19(2):46-53.

Deval R, Saxena P, Pradhan D, Mishra AK, Jain AK. A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns. Indian J Pediatr. 2022;89(11):1140-3.

Pini N, Lucchini M, Esposito G, Tagliaferri S, Campanile M, Magenes G, Signorini MG. A Machine Learning Approach to Monitor the Emergence of Late Intrauterine Growth Restriction. Front Artif Intell. 2021;4:622616.

Rescinito R, Ratti M, Payedimarri AB, Panella M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare (Basel). 2023;11(11):1617.

WHO. Nutrition Landscape Information System (NLiS) - Low birth weight: WHO, 2023. Available at:,growth%20restriction%2C%20prematurity%20or%20both. Accessed on 04 August 2023.

Khan W, Zaki N, Masud MM, Ahmad A, Ali L, Ali N, et al. Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms. Sci Rep. 2022;12(1):12110.

Eliyati N, Faruk A, Kresnawati E, Arifieni I. Support vector machines for classification of low birth weight in Indonesia. J Physic Conf Ser. 2019;1282:012010.

Arayeshgari M, Najafi-Ghobadi S, Tarhsaz H, Parami S, Tapak L. Machine Learning-based Classifiers for the Prediction of Low Birth Weight. Healthc Inform Res. 2023;29(1):54-63.

Rodríguez E, Aguirre Rodríguez E, Nascimento L, Silva A, Marins F. A data-driven approach for neonatal mortality rate forecasting. 2022.






Review Articles