Authors :
Maureen I. Akazue; Geofrey A. Nwokolo; Okpako A. Ejaita; Clement O. Ogeh; Emmanuel Ufiofio
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3LE0hqt
DOI :
https://doi.org/10.5281/zenodo.7937239
Abstract :
Developing effective survival analysis models
would help guide the decision-making in managing
major health challenges. Model development can be
achieved through various approaches. Diabetes is a
health challenge in Nigeria that has attracted the interest
of researchers thus much research has been carried out
as regards its management necessitating the
development of models. This study carried out a machine
learning analysis on diabetes data collected from Central
Hospital, Warri, Delta State implementing Cox-PH
Model due to the role both play in survival analysis. A
dataset of 100 diabetic patients' records was collected.
The dataset was used for training multiple machine
learning algorithms, namely, SupportVector (SVM), Knearestneighbors (KNN) classifier, etc., and the proposed
model (Cox-PH Hybrid or CPH-SML). The performance
evaluation of the machine learning algorithms and the
proposed model gave accuracy levels as follows: KNN47%, SVM; 74%, and Cox-PH Hybrid-96%. The
concordance index was used to evaluate the proposed
model and it had an index of 0.7204, on several
covariates such as Age, Gender, Education, Marital
Status, history of smoking, SBP, DBP, etc. From this
study's analysis of the diabetic data, it was able to
conclude that the variables associated with diabetes
mortality are; the age of the patient and diabetes types.
The patients' hazard ratio reduces when they are young
compared to when they are old. The patient's hazard
ratio is also dependent on the diabetes type. Thus, early
diagnosis and proper health management of diabetics
can prolong the age of diabetic patients.
Keywords :
Survival Model, Machine Learning, Cox Proportional Hazard, Diabetes.
Developing effective survival analysis models
would help guide the decision-making in managing
major health challenges. Model development can be
achieved through various approaches. Diabetes is a
health challenge in Nigeria that has attracted the interest
of researchers thus much research has been carried out
as regards its management necessitating the
development of models. This study carried out a machine
learning analysis on diabetes data collected from Central
Hospital, Warri, Delta State implementing Cox-PH
Model due to the role both play in survival analysis. A
dataset of 100 diabetic patients' records was collected.
The dataset was used for training multiple machine
learning algorithms, namely, SupportVector (SVM), Knearestneighbors (KNN) classifier, etc., and the proposed
model (Cox-PH Hybrid or CPH-SML). The performance
evaluation of the machine learning algorithms and the
proposed model gave accuracy levels as follows: KNN47%, SVM; 74%, and Cox-PH Hybrid-96%. The
concordance index was used to evaluate the proposed
model and it had an index of 0.7204, on several
covariates such as Age, Gender, Education, Marital
Status, history of smoking, SBP, DBP, etc. From this
study's analysis of the diabetic data, it was able to
conclude that the variables associated with diabetes
mortality are; the age of the patient and diabetes types.
The patients' hazard ratio reduces when they are young
compared to when they are old. The patient's hazard
ratio is also dependent on the diabetes type. Thus, early
diagnosis and proper health management of diabetics
can prolong the age of diabetic patients.
Keywords :
Survival Model, Machine Learning, Cox Proportional Hazard, Diabetes.
