An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers

Dhruva Darshan B S, Punit Sharma, Krishnaraj Chadaga, Niranjana Sampathila*, G. Muralidhar Bairy, Sushma Belurkar*, Srikanth Prabhu, Swathi K S

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number2420927
JournalSystems Science and Control Engineering
Volume12
Issue number1
DOIs
Publication statusPublished - 2024

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization
  • Artificial Intelligence

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