DIABETES MELLITUS (DM) PREDICTION
USING MACHINE LEARNING
SARAVANAN SUKUMARAN
S2155503
CONTENT
01
02
INT R O DUCTION
03
04
05
R ESEAR CH QUESTION AND R ESEAR CH O BJECT I VE
06
MET H O DO LO GY
07
08
R ESEAR CH LIMIT AT IONS
P R O BLEM ST AT EMENT
R ESEAR CH SCOPE AND CONTR IBUTION
LIT ER AT UR E R EVIEW
CO NCLUSION
INTRODUCTION
Understanding
Diabetes Mellitus
Classified into Type 1
(insufficient insulin
production) and Type 2
(insulin resistance).
Importance of
Early Detection
Need for innovative methods to detect
Diabetes Mellitus early and prevent major
health issues.
(American Diabetes Association, 2017)
Role of Machine Learning
in Detecting Diabetes
Mellitus
Recent advancements
in machine learning
have enabled us to
predict and detect
Diabetes Mellitus more
effectively.
PROBLEM STATEMENT
Traditional detecti o n m eth o ds rel y o n cli ni cal vi si ts and
tes ts , po tenti ally l eadi ng to l ate detecti o n and treatm ent .
Recent advancements i n machi ne l earning o ffer pro misi ng
al ternatives for the earl y predi cti o n an d managem ent o f
thes e
conditions .
Ho wev er,
chall enges
remai n
in
unders tanding whi ch facto rs can be effecti v el y utilized by
thes e m odel s for predi cti ng the p rev al ence acco rdi ng to the
type o f Diabetes Mel l i tus .
( Am eri can D i abetes As s o ci ati on, 2017)
RESEARCH QUESTION
Research
Question
• What factors can machi ne learni ng models use to predi ct the
prevalence of Type 1 and Type 2 Di abetes Melli tus?
• How accurate are machi ne learni ng models i n predi cti ng the
prevalence of Type 1 and Type 2 Di abetes Melli tus?
• What are the advantages of usi ng machi ne learni ng models to
predi ct and manage the prevalence of Type 1 and Type 2 Di abetes
Me lli tus?
• To id en tify th e factor s th at can b e u s ed to p r ed ict th e p r evalen ce
of Type 1 and Type 2 Diabetes Mellitus us ing machine learning
mod els .
• To evalu ate th e accu r acy of d iffer en t mach in e lear n in g mod els in
pred ictin g th e p r evalen ce of Ty p e 1 an d Ty p e 2 Diab etes Mellitu s .
• To exp lor e th e p oten tial b en efits of u s in g mach in e lear n in g
mod els in p r ed ictin g an d man agin g th e p r evalen ce of Ty p e 1 an d
Type 2 Diab etes Mellitu s .
RESEARCH OBJECTIVE
Research
Objective
RESEARCH SCOPE AND CONTRIBUTION
• The research broadens the scope of existing studies by encompassing both Type 1 and
Type 2 Diabetes Mellitus, aiming to understand the efficacy of machine learning
algorithms in predicting and managing these conditions using a comprehensive
patient records dataset.
• The research will compare different machine learning models to determine the most
effective one, as well as explore the potential benefits of these algorithms for
predicting and managing both types of Diabetes Mellitus.
• This research's contribution lies in advancing Diabetes Mellitus research through the
development of accurate predictive models, potentially improving disease
management, enhancing patient outcomes, and reducing healthcare costs.
METHODOLOGY
Research Type: • Quantitative Research Approach
• Inferential Research Design
Train-Test Split 70-30
UCI Machine Learning
Repository
National Health and
Nutrition Examination
Survey (NHANES)
Data Cleaning
Data Normalization
Feature Engineering
DATA SOURCE
Models Deployment:
• RF
• SVM
• KNN
• ANN
• LR
Accuracy
Interpretation
Discussion
MODELLING
PRE-PROCESSING
EVALUATION
Objective 1
Objective 2
Objective 3
DATA DESCRIPTION
Sample Size: 768 Rows
Attributes: 9
IDENTIFY TYPE 1 AND TYPE 2 DIABETES
The Diabetes Pedigree ( Family History) is a factor in helping
to identify whether a person has type 1 or type 2 diabetes
RESEARCH LIMITATIONS
The dataset used in this study was small, and larger datasets
may improve the model's ability to make accurate predictions.
The research was limited to machine learning models. However,
with a larger and more diverse dataset, the research could be
expanded to include deep learning models, which could lead to
more accurate results.
CONCLUSION
Prediction of type 1 and type 2 diabetes can
help identify the factors that lead to each
type, which can then be used to develop
more effective treatments.
Machine learning models can be used to
predict both types of diabetes, which can
help to improve patient outcomes and
reduce the risk of complications.
REFERENCES
• Prakash, BBNS. et al. (2023) ‘Comparative performance analysis of quantum algorithm with machine learning algorithms
on diabetes mellitus’, 2023
International Conference on Intelligent and Innovative Technologies in Computing,
Electrical and Electronics (IITCEE) [Preprint]. https://doi:10.1109/iitcee57236.2023.10090957.
• Charitha, C. et al. (2022) ‘Type-II diabetes prediction using machine learning algorithms, 2022 International Conference
on Computer Communication and Informatics (ICCCI) [Preprint]. https://doi:10.1109/iccci54379.2022.9740844.
• Khanam, J.J. and Foo, S.Y. (2021) ‘A comparison of machine learning algorithms for diabetes prediction’, ICT Express, 7(4),
pp. 432–439. https://doi:10.1016/j.icte.2021.02.004.
• Pal, M., Parija, S., & Panda, G. (2021). Improved Prediction of Diabetes Mellitus using Machine Learning Based Approach.
2021 2nd International Conference on Range Technology (ICORT). https://doi.org/10.1109/icort52730.2021.9581774.
• American Diabetes Association. (2017). 2. Classification and Diagnosis of Diabetes:Standards of Medical Care in
Diabetes—2018. Diabetes Care. Retrieved April 10, 2023, https://doi.org/10.2337/dc18-s002
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