Meet SHANU OLANIYI RILWAN, an Academic Staff of Lagos State University.
Software Engineering, Health Informatics, Machine Learning, Soft Computing, Expert System
Assistant Lecturer
Computer Science
At the Computer Science department office
Appointment on Visitation important
Topic: Expert System Using Fuzzy Logic FCM
Description: My research interest and focus is on Intuitionistic fuzzy similarity measure which gives Boolean value 0-1 but considers both membership and non-membership functions. This has been used to resolve classification problems but with shortcomings. The interest in this area is to research into resolving some of these lapses. Also, my interest on Neural Network, Data Science and Analysis; including application of statistical, mathematical modeling and machine learning analyses for solving real life complex problems.
| # | Certificate | School | Year |
|---|---|---|---|
| 1. | M.Sc (Computer Science) | Computer Science, Lagos State University, Ojo Lagos. | 2018 |
A Framework for the Development of a Decision Support System for Autopsy Services DSS-AS
An autopsy report, also called a postmortem examination, is a medical procedure conducted to investigate the cause of death in deceased individuals. The autopsy service or process is a critical component of healthcare which helps to provide valuable insights into patient care and facilitate quality improvement initiatives. However, the complexity and variability of autopsy procedures can lead to inefficiencies and inconsistencies due to the manual interpretation of the procedure. This study proposes a framework for the development of a decision support system DSS for autopsy services. The proposed DSS-AS will leverage data analytics, machine learning algorithms, and expert knowledge to provide real-time guidance and recommendations for autopsy procedures for pathologists and other medical-expert physicians. The framework consists of four 4 primary components: Data Integration, Decision Algorithms, Expert Knowledge, and User Interface. In this study, we proposed to analyze a combined dataset between 500-1500 of autopsy cases from LASUTH Ikeja and LUTH Idi-araba, both in Lagos Nigeria, comprising of demographic, clinical and pathological data. The scope of this study is constrained to adult autopsy cases 60years+ conducted within the last five years. The model will be tested using a multi-method approach, integrating ML algorithms SVM, RF, DT, and GB with expert knowledge and rule-based systems to develop the DSS-AS. The framework will be evaluated using performance metrics such as accuracy, precision, recall, F1-score and Expert Acceptance Testing EAT . The proposed DSS primary aim will be to enhance the accuracy, efficiency, and consistency of autopsy services, ultimately improving patient care and outcomes, thus, providing a structured framework for the development of the DSS-AS. This study has potential to contribute to the advancement of quality autopsy services and supports the integration of data-driven decision-making in healthcare, thereby making informed decisions and reducing errors.
SHANU RILWAN is a Assistant Lecturer at the Department of Computer Science
SHANU has a M.Sc in Computer Science from Computer Science, Lagos State University, Ojo Lagos.
