- Location
- Calgary, Alberta, Canada
- Bio
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I am a data scientist and a seasoned professional in public health with over a decade of experience.
My educational background includes a bachelor’s degree in medical sciences from Ambrose Alli university Ekpoma, specializing in Histopathology, and a diploma in Data Science from Lighthouse Labs.
I began my professional career in 2005, working in Medical Laboratories at tertiary and secondary hospitals until 2010.
In 2011, I transitioned into public health, starting as a volunteer, and progressing to become a Program Manager for various non-profit organizations.
Noteworthy projects I managed include initiatives such as HIV/AIDS, TB, Maternal & Child health, etc., funded by USAID, WHO, Global Funds, Jhpiego, UNDP and EU.
My skills in Data Science and Public Health include effective program management, monitoring and evaluation, report writing, data analysis and visualization using BI tools. I also possess programming and machine learning expertise with python for data-driven decision-making.
Outside of work, I enjoy staying informed about political news, playing chess and soccer. - Portals
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Toronto, Ontario, Canada
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- Categories
- Data visualization Data analysis Data modelling Public health Data science
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Recent projects
Personal projects
Analysis on Liver Disease Diagnosis and Prediction
August 2023 - August 2023
https://github.com/Ikennaodinye/Final_Project_Liver_DiagnosisProblem: Accurate and early diagnosis of liver disease is critical for effective treatment and patient well-being.
Motivation: Improving diagnosis can lead to better healthcare outcomes, reduced medical costs, and enhanced patient care.
Diagnosis:
Blood tests or Liver function tests which is the basis of this project in the major way of diagnosing liver disease measures enzymes, bilirubin, and proteins to assess how well the liver is functioning.
Project goals & objectives:
Improving Healthcare service delivery - Contribute to enhanced patient care, reduced medical costs, and improved healthcare outcomes through reliable diagnosis.
Early Detection - Identify features that contribute to early detection of liver disease, enabling timely medical interventions.
Accurate Diagnosis - Develop predictive models to accurately diagnose liver disease based on patient data.