Title:A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes
Volume: 17
Issue: 2
Author(s): Nitigya Sambyal*, Poonam Saini and Rupali Syal
Affiliation:
- Department of Computer Science & Engineering, Punjab Engineering College, Sector 12, Chandigarh-160012,India
Keywords:
Microvascular complications, retinopathy, nephropathy, neuropathy, machine learning, statistical analysis.
Abstract:
Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged
as a serious public health issue worldwide. According to the World Health Organization (WHO), without
interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled
diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels, and
nerves.
Methods: The paper presents a critical review of existing statistical and Artificial Intelligence (AI)
based machine learning techniques with respect to DM complications, mainly retinopathy, neuropathy,
and nephropathy. The statistical and machine learning analytic techniques are used to structure the
subsequent content review.
Results: It has been observed that statistical analysis can help only in inferential and descriptive analysis
whereas, AI-based machine learning models can even provide actionable prediction models for
faster and accurate diagnosis of complications associated with DM.
Conclusion: The integration of AI-based analytics techniques, like machine learning and deep learning
in clinical medicine, will result in improved disease management through faster disease detection and
cost reduction for the treatment.
