Darshan BSD, Sampathila N, Bairy MG, Belurkar S, Prabhu S, Chadaga K. Detection of anemic condition in patients from clinical markers and explainable artificial intelligence.
Technol Health Care 2024;
32:2431-2444. [PMID:
38339945 DOI:
10.3233/thc-231207]
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Abstract
BACKGROUND
Anaemia is a commonly known blood illness worldwide. Red blood cell (RBC) count or oxygen carrying capability being insufficient are two ways to describe anaemia. This disorder has an impact on the quality of life. If anaemia is detected in the initial stage, appropriate care can be taken to prevent further harm.
OBJECTIVE
This study proposes a machine learning approach to identify anaemia from clinical markers, which will help further in clinical practice.
METHODS
The models are designed with a dataset of 364 samples and 12 blood test attributes. The developed algorithm is expected to provide decision support to the clinicians based on blood markers. Each model is trained and validated on several performance metrics.
RESULTS
The accuracy obtained by the random forest, K nearest neighbour, support vector machine, Naive Bayes, xgboost, and catboost are 97%, 98%, 95%, 95%, 98% and 97% respectively. Four explainers such as Shapley Additive Values (SHAP), QLattice, Eli5 and local interpretable model-agnostic explanations (LIME) are explored for interpreting the model predictions.
CONCLUSION
The study provides insights into the potential of machine learning algorithms for classification and may help in the development of automated and accurate diagnostic tools for anaemia.
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