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Analyzing and Predicting Anaemia with Advanced Machine Learning Techniques with Comparative Analysis

by Sandeep Kumar Chundru, Mukund Sai Vikram Tyagadurgam, Venkataswamy Naidu Gangineni, Sriram Pabbineedi, Ajay Babu Kakani, Sri Krishna Kireeti Nandiraju
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 13 - Number 1
Year of Publication: 2025
Authors: Sandeep Kumar Chundru, Mukund Sai Vikram Tyagadurgam, Venkataswamy Naidu Gangineni, Sriram Pabbineedi, Ajay Babu Kakani, Sri Krishna Kireeti Nandiraju
10.5120/ijais2025452027

Sandeep Kumar Chundru, Mukund Sai Vikram Tyagadurgam, Venkataswamy Naidu Gangineni, Sriram Pabbineedi, Ajay Babu Kakani, Sri Krishna Kireeti Nandiraju . Analyzing and Predicting Anaemia with Advanced Machine Learning Techniques with Comparative Analysis. International Journal of Applied Information Systems. 13, 1 ( Aug 2025), 28-36. DOI=10.5120/ijais2025452027

@article{ 10.5120/ijais2025452027,
author = { Sandeep Kumar Chundru, Mukund Sai Vikram Tyagadurgam, Venkataswamy Naidu Gangineni, Sriram Pabbineedi, Ajay Babu Kakani, Sri Krishna Kireeti Nandiraju },
title = { Analyzing and Predicting Anaemia with Advanced Machine Learning Techniques with Comparative Analysis },
journal = { International Journal of Applied Information Systems },
issue_date = { Aug 2025 },
volume = { 13 },
number = { 1 },
month = { Aug },
year = { 2025 },
issn = { 2249-0868 },
pages = { 28-36 },
numpages = {9},
url = { https://www.ijais.org/archives/volume13/number1/analyzing-and-predicting-anaemia-with-advanced-machine-learning-techniques-with-comparative-analysis/ },
doi = { 10.5120/ijais2025452027 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-29T00:23:15.253355+05:30
%A Sandeep Kumar Chundru
%A Mukund Sai Vikram Tyagadurgam
%A Venkataswamy Naidu Gangineni
%A Sriram Pabbineedi
%A Ajay Babu Kakani
%A Sri Krishna Kireeti Nandiraju
%T Analyzing and Predicting Anaemia with Advanced Machine Learning Techniques with Comparative Analysis
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 13
%N 1
%P 28-36
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The rising prevalence of anaemia poses significant health challenges globally, necessitating accurate and timely diagnostic methods. This work applies the AdaBoost classification model to a Complete Blood Count (CBC) dataset to provide a reliable machine learning method for anaemia prediction. The methodology used consists of extensive pre-processing including data cleaning, one-hot encoding, z score normalization and automated feature selection to preserve the data's integrity and make the model simpler. The experimental results proved that the presented AdaBoost model achieved a promising accuracy of 92.7%, good precision of 84%, fair recall of 94% and good F1 value of 88.7%, indicating effectively well-balanced classification performance. Further, ROC curve analysis, with AUC of 0.94, confirms superior discriminatory capability. It compared their results to existing models, LogitBoost and Random Forest (RF); LogitBoost yields an accuracy slightly low at 89.3% and RF at 67.1%. The results highlight the capabilities of the AdaBoost model for early, accurate anaemia detection, providing substantial improvements over conventional diagnostic measures and improving clinical decision making in real time healthcare applications.

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Index Terms

Computer Science
Information Sciences

Keywords

Anaemia Detection AdaBoost CBC Dataset Diagnostic Accuracy Predictive Analytics Health Informatics

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