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Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity

Fahmida Haque¹, Mamun B I Reaz¹, Muhammad E H Chowdhury²

¹Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Computational Intelligence and Neuroscience

|May 5, 2022

Summary

Machine learning models accurately predict diabetic sensorimotor polyneuropathy (DSPN) using nerve conduction studies (NCS). An ensemble classifier achieved 93.40% accuracy, enhancing DSPN management.

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Area of Science:

Neurology
Medical Informatics

Background:

Diabetic sensorimotor polyneuropathy (DSPN) is a common complication in long-term diabetes patients.
Current application of machine learning (ML) for DSPN diagnosis using nerve conduction studies (NCS) is limited in research.

Purpose of the Study:

To investigate the efficacy of ML models in diagnosing DSPN using NCS data.
To evaluate different feature ranking and classification techniques for DSPN detection.

Main Methods:

Nerve conduction study (NCS) data were collected from the DCCT and EDIC clinical trials.
Ten NCS variables were analyzed using three feature ranking techniques and eight conventional classifiers.
Ensemble and random forest models were evaluated for their diagnostic performance.

Main Results:

The ensemble classifier achieved 93.40% accuracy, 91.77% sensitivity, and 98.44% specificity.
The random forest model showed 93.26% accuracy, 91.95% sensitivity, and 98.95% specificity.
Both models demonstrated a kappa value of 0.82, indicating good agreement and accuracy in DSPN identification.

Conclusions:

The ensemble classifier, utilizing all ten NCS variables, can effectively predict DSPN severity.
This ML-based approach can significantly enhance the management of patients with DSPN.