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Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.

Sonia Darvishi1, Omid Hamidi2, Jalal Poorolajal3

  • 1Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran.

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The Random Forest model shows superior performance in early Multiple Sclerosis (MS) prediction compared to traditional methods. This machine learning approach aids in accurate diagnosis and disease management.

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Area of Science:

  • Neurology
  • Medical Informatics
  • Biostatistics

Background:

  • Multiple Sclerosis (MS) poses a significant health risk in Hamedan Province, Iran.
  • Accurate early diagnosis is crucial for effective Multiple Sclerosis (MS) management.
  • Developing predictive systems for MS is a key area of research.

Purpose of the Study:

  • To compare the predictive performance of machine learning techniques against traditional methods for Multiple Sclerosis (MS).
  • To identify the most effective classification model for early MS detection.

Main Methods:

  • A case-control study involving 200 patients in Hamadan, Western Iran (2013-2015).
  • Evaluation of six classifiers based on sensitivity, specificity, PPV, NPV, LR+, LR-, and overall accuracy.
  • Utilized machine learning algorithms including Random Forest (RF).

Main Results:

  • The Random Forest (RF) model demonstrated superior performance across various metrics, including specificity (0.67), PPV (0.68), and total accuracy (0.68).
  • Age, birth season, and gender were identified as the most influential factors in predicting MS.
  • All evaluated methods showed similar performance, with RF exhibiting a slight advantage.

Conclusions:

  • The Random Forest (RF) model is an effective classifier for the early prediction of Multiple Sclerosis (MS).
  • Early diagnosis using advanced methods like RF can significantly aid in disease control.
  • Machine learning offers a promising avenue for improving diagnostic accuracy in neurological disorders.