Iraqi newborns mortality prediction using naive bayes classifier

Designing an electronic application to predict and warn of neonatal mortality based on mother data and analyses it using internationally approved techniques and algorithms for the purpose of alerting future mothers to the risks that may lead to neonatal mortality by focusing on the causes in previous similar cases in the Najaf governorate and it is possible to apply searching on a broader segment and according to the availability of data on that at the level of Iraq, for example, since such systems are currently applied in developed countries which need to apply them in Iraq in a manner appropriate to the nature and status of Iraq. The goals of medical systems designed in developed countries are summarized by providing requirements for improving medical services, taking into account their guarantee for all groups and different places in the country. Contributions of this paper are proposed new health system predicting model for Iraqi newborn mortality with best and latest methods available which need for this system, naïve Bayes Abstract

[1]  K. Fennie,et al.  Health system reconstruction: Perspectives of Iraqi physicians , 2010, Global public health.

[2]  Muthuraman Thangaraj,et al.  Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA , 2018 .

[3]  R. Garfield,et al.  The effect of economic sanctions on the mortality of Iraqi children prior to the 1991 Persian Gulf War. , 2000, American journal of public health.

[4]  Ali Kashif Bashir,et al.  A hybrid egocentric video summarization method to improve the healthcare for Alzheimer patients , 2019, J. Ambient Intell. Humaniz. Comput..

[5]  S. Pal,et al.  Prediction of benign and malignant breast cancer using data mining techniques , 2018 .

[6]  Massimiliano Pontil,et al.  Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction , 2019, ICML.

[7]  Francis R. Bach,et al.  On Structured Prediction Theory with Calibrated Convex Surrogate Losses , 2017, NIPS.

[8]  Tao Li,et al.  Recent advances in feature selection and its applications , 2017, Knowledge and Information Systems.

[9]  Mukesh M. Raghuwanshi,et al.  Hyperparameter Tuning and Optimization in Machine Learning for Species Identification System , 2020 .

[10]  M. Welcome.  The Nigerian health care system: Need for integrating adequate medical intelligence and surveillance systems , 2011, Journal of pharmacy & bioallied sciences.

[11]  C. Aziz.  Struggling to rebuild Iraq's health-care system. War, sanctions, and mismanagement have left health system in shambles. , 2003, Lancet.

[12]  Deepak Gupta,et al.  Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model , 2020, Pattern Recognit. Lett..

[13]  Verónica Bolón-Canedo,et al.  On the scalability of feature selection methods on high-dimensional data , 2017, Knowledge and Information Systems.

[14]  Ali Kashif Bashir,et al.  Optimal Haptic Communications Over Nanonetworks for E-Health Systems , 2019, IEEE Transactions on Industrial Informatics.

[15]  Ali R Tawfik-Shukor,et al.  The impact of health system governance and policy processes on health services in Iraqi Kurdistan , 2010, BMC international health and human rights.

[16]  Yiannis Demiris,et al.  A Structured Prediction Approach for Conditional Meta-Learning , 2020, ArXiv.

[17]  Wang Yong,et al.  Socio-Technological Factors Affecting User’s Adoption of eHealth Functionalities: A Case Study of China and Ukraine eHealth Systems , 2019, IEEE Access.

[18]  Mehdi Moradinazar,et al.  Dietary patterns, nutrition, and risk of breast cancer: a case-control study in the west of Iran , 2019, Epidemiology and health.

[19]  N. Shabila,et al.  Iraqi health system in kurdistan region: medical professionals' perspectives on challenges and priorities for improvement , 2010, Conflict and health.

[20]  Ndow As,et al.  The Gambia Multiple Indicator Cluster Survey 2005 / 2006 report. Monitoring the situation of children and women. , 2007 .