Comprehensive Analysis of Diabetes Prediction System: Machine Learning and Deep Learning

Disease management and clinical diagnostics are two significant problems in the medical field and have a constructive effect on the public health system. Diabetes mellitus (DM) is a typical metabolic disease; people face an increased level of blood sugar (Persistent hyperglycemia). It has an impact on many parts of the body including the heart, kidney,eyes, foot, skin, etc. Several compey Diabetes Diagnosis Systems (DDSs) use various Machine Learning (ML) techniques for deriving useful information from clinical datasets for DDSs and the disease. However, trapping into local optimum solution, absence of privacy, missing value in input dataset, and deficiency of incremental classification are main limitations associated with conventional diabetes classification algorithms based on ML.

Authors

Publication Details

Journal
Panamerican mathematical journal.
Published
2024-11-20
DOI
https://doi.org/10.52783/pmj.v35.i2s.2399
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Comprehensive Analysis of Diabetes Prediction System: Machine Learning and Deep Learning

T. Nedunchezhian
Panamerican mathematical journal.
Artificial Intelligence in Healthcare
article

Comprehensive Analysis of Diabetes Prediction System: Machine Learning and Deep Learning

T. Nedunchezhian
article en

Abstract

Disease management and clinical diagnostics are two significant problems in the medical field and have a constructive effect on the public health system. Diabetes mellitus (DM) is a typical metabolic disease; people face an increased level of blood sugar (Persistent hyperglycemia). It has an impact on many parts of the body including the heart, kidney,eyes, foot, skin, etc. Several compey Diabetes Diagnosis Systems (DDSs) use various Machine Learning (ML) techniques for deriving useful information from clinical datasets for DDSs and the disease. However, trapping into local optimum solution, absence of privacy, missing value in input dataset, and deficiency of incremental classification are main limitations associated with conventional diabetes classification algorithms based on ML.

Panamerican mathematical journal.Vol. 35(2s)
Openalex Percentile: Top 63%
Artificial Intelligence in Healthcare
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