A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients

Chadaga, Krishnaraj; Prabhu, Srikanth; Sampathila, Niranjana; Chadaga, Rajagopala

doi:10.1016/j.health.2023.100170

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…Eli5 is yet another method to demystify predictions 44 . It is a python package and is highly used with tree-based classifiers.…”

Section: Resultsmentioning

confidence: 99%

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Chadaga,

Prabhu,

Sampathila

et al. 2024

Sci Rep

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The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.

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“…Eli5 is yet another method to demystify predictions 44 . It is a python package and is highly used with tree-based classifiers.…”

Section: Resultsmentioning

confidence: 99%

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Chadaga,

Prabhu,

Sampathila

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…We can more effectively understand the significance of various characteristics using the feature importance techniques mentioned above. The final stacking models of Mutual information, Pearson's correlation, Particle swarm optimization and Harris Hawks algorithm were employed for interpretation [46]. SHAP uses each feature's significance to the model's prediction to explain the machine learning model's output [47].…”

Section: Explainable Artificial Intelligence (Xai)mentioning

confidence: 99%

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Chadaga,

Sampathila

et al. 2023

Information

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Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment.

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Predicting acute myocardial infarction from haematological markers utilizing machine learning and explainable artificial intelligence

Bhat,

Chadaga,

Sampathila

et al. 2024

Systems Science & Control Engineering

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A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients

Cited by 7 publications

References 38 publications

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Predicting acute myocardial infarction from haematological markers utilizing machine learning and explainable artificial intelligence

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