TY - JOUR
T1 - An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers
AU - B S, Dhruva Darshan
AU - Sharma, Punit
AU - Chadaga, Krishnaraj
AU - Sampathila, Niranjana
AU - Bairy, G. Muralidhar
AU - Belurkar, Sushma
AU - Prabhu, Srikanth
AU - K S, Swathi
N1 - Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.
AB - Anaemia is a disorder marked by low blood levels of haemoglobin (HGB), affecting people of all ages and ethnicities and is a major global public health concern. Anaemia must be diagnosed as soon as possible to enable prompt treatment and intervention, which can reduce complications and enhance patient outcomes. With the ability to improve diagnostic precision and expedite patient care procedures, machine learning (ML) has become a potent instrument in the healthcare industry. Hence, we examine the use of ML approaches to predict haemoglobin-like anaemia in this research article. Based on a heterogeneous dataset of blood markars, we investigate the performance of many machine learning techniques such as Logistic Regression, CatBoost, XgBoost Decision Trees, KNN and others. The algorithms are further ensembled using a customized stacking approach. The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. The xgboost and the stacking classifier obtained an accuracy, precision and recall of 99% each. Our research shows how ML models can help with the early diagnosis and treatment of anaemia, which will ultimately lead to better patient outcomes and healthcare results. Overall, the research shows how ML emphasizes the value of interdisciplinary cooperation in solving challenging medical problems.
UR - http://www.scopus.com/inward/record.url?scp=85208800653&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85208800653&partnerID=8YFLogxK
U2 - 10.1080/21642583.2024.2420927
DO - 10.1080/21642583.2024.2420927
M3 - Article
AN - SCOPUS:85208800653
SN - 2164-2583
VL - 12
JO - Systems Science and Control Engineering
JF - Systems Science and Control Engineering
IS - 1
M1 - 2420927
ER -