Authors :
Oladejo, Rachel Adefunke; Engr. Oyedeji Ayo Isaac; Engr. Oluleye Gabriel; Engr. Akinrogunde Oluwadare Olatunde; Adenle Bamidele. J
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/z4zdb5fs
Scribd :
https://tinyurl.com/5n8jb85b
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR2040
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The primary risk factors for patients with
Knee Osteoarthritis (KOA) were determined in this
study, and a predictive model was developed using the
data found. In order to comprehend the body of
information regarding musculoskeletal-related diseases,
a thorough study of relevant literature was conducted.
One ailment that falls within the musculoskeletal
category is knee osteoarthritis, and the risk factors were
extracted and confirmed by medical professionals.
clinical data encompassing characteristics tracked
during KOA patients' treatment were gathered from
Ile-Ife, Osun State, Nigeria at the OAU Teaching
Hospital Complex (OAUTHC), , as well as from a few
other people Utilizing questionnaires, . For this
investigation, the entire dataset comprising data on 83
patients was used. WEKA software was used to
compare four supervised machine learning techniques
so as to create the model. The accuracy of the was
97.59% when examining the 36 originally identified
attributes without selecting any featue. The outcomes
additionally demonstrated The minimal amount of
variables pertinent to the osteoarthritis condition of the
knee. Subsequent findings demonstrated the relevance
of each feature found in order to create a prognosis
model for knee osteoarthritis that is both effective and
efficient. Age is the most important factor for KOA,
according to the study's findings, and all 36
characteristics were found to be useful in forecasting
the likelihood of Knee Osteoarthritis..
Keywords :
Prognostic Model, Supervised Machine Learning, Knee Osteoarthritis.
The primary risk factors for patients with
Knee Osteoarthritis (KOA) were determined in this
study, and a predictive model was developed using the
data found. In order to comprehend the body of
information regarding musculoskeletal-related diseases,
a thorough study of relevant literature was conducted.
One ailment that falls within the musculoskeletal
category is knee osteoarthritis, and the risk factors were
extracted and confirmed by medical professionals.
clinical data encompassing characteristics tracked
during KOA patients' treatment were gathered from
Ile-Ife, Osun State, Nigeria at the OAU Teaching
Hospital Complex (OAUTHC), , as well as from a few
other people Utilizing questionnaires, . For this
investigation, the entire dataset comprising data on 83
patients was used. WEKA software was used to
compare four supervised machine learning techniques
so as to create the model. The accuracy of the was
97.59% when examining the 36 originally identified
attributes without selecting any featue. The outcomes
additionally demonstrated The minimal amount of
variables pertinent to the osteoarthritis condition of the
knee. Subsequent findings demonstrated the relevance
of each feature found in order to create a prognosis
model for knee osteoarthritis that is both effective and
efficient. Age is the most important factor for KOA,
according to the study's findings, and all 36
characteristics were found to be useful in forecasting
the likelihood of Knee Osteoarthritis..
Keywords :
Prognostic Model, Supervised Machine Learning, Knee Osteoarthritis.
