Classification of Hematological Data Using Data Mining Technique to Predict Diseases

Akter, Fahmida; Hossin, Md Altab; Daiyan, Golam Moktader; Hossain, Md. Motaher

doi:10.4236/jcc.2018.64007

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…They used MATLAB to carry out their study and analysis. Another research on the application of data mining techniques in illness prediction was conducted by [33]. They used the WEKA analysis tool to examine several categorization algorithms in their study effort to find the optimum end-user functionality for hematological data.…”

Section: Related Workmentioning

confidence: 99%

“…In another type, RF is just an ensemble of unpruned classification trees, which is preferred for its greater performance on real-world problems, as well as its lack of sensitivity to input noise and resistance to overfitting [51]. RF is a supervised learning approach used for both classification and prediction regression issues, according to [4,[33][34][35]. According to their findings, a typical forest consists of a variety of trees, and the more trees in a forest, the more resilient the forest.…”

Section: Random Forest Algorithm (Rf)mentioning

confidence: 99%

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An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

Ayoade,

Oladele,

Lucky Imoize

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The human blood as a collection of tissues containing Red Blood Cells (RBCs), circular in shape and acting as an oxygen carrier, are frequently deformed by multiple blood diseases inherited from parents. These hereditary diseases of blood involve abnormal haemoglobin (Hb) or anemia which are major public health issues. Sickle Cell Disease (SCD) is one of the common non-communicable disease and genetic disorder due to changes in hematological conditions of the RBCs which often causes the inheritance of mutant Hb genes by the patient.. OBJECTIVES: The process of manual valuation, predictions and diagnosis of SCD necessitate for a passionate time spending and if not done properly can lead to wrong predictions and diagnosis. Machine Learning (ML), a branch of AI which emphases on building systems that improve performance based on the data they consume is appropriate. Despite previous research efforts in predicting with single ML algorithm, the existing systems still suffer from high false and wrong predictions. METHODS: Thus, this paper aimed at performing comparative analysis of individual ML algorithms and their ensemble models for effective predictions of SCD (elongated shapes) in erythrocytes blood cells. Three ML algorithms were selected, and ensemble models were developed to perform the predictions and metrics were used to evaluate the performance of the model using accuracy, sensitivity, Receiver Operating Characteristics-Area under Curve (ROC-AUC) and F1 score metrics. The results were compared with existing literature for model(s) with the best prediction metrics performance.. RESULTS: The analysis was carried out using Python programming language. Individual ML algorithms reveals that their accuracies show MLR=87%, XGBoost=90%, and RF=93%, while hybridized RF-MLR=92% and RF-XGBoost=99%. The accuracy of RF-XGBoost of 99% outperformed other individual ML algorithms and Hybrid models. CONCLUSION: Thus, the study concluded that involving hybridized ML algorithms in medical datasets increased predictions performance as it removed the challenges of high variance, low accuracy and feature noise and biases of medical datasets. The paper concluded that ensemble classifiers should be considered to improve sickle cell disease predictions.

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Section: Related Workmentioning

confidence: 99%

Section: Random Forest Algorithm (Rf)mentioning

confidence: 99%

An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

Ayoade,

Oladele,

Lucky Imoize

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…Data mining is a technique utilized to create applications in the field of medicine. Data mining is used in the healthcare industry to help healthcare professionals provide adequate care, which benefits patients by reducing costs and ensuring quality care [8]. Health-related research projects also make use of data mining techniques, particularly categorization and prediction [9].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Setiawan,

Amalia,

Prasetiawan

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding the different types of anemia is vital to tailor effective prevention and treatment. This research explores the role of data mining in predicting and classifying anemia types, focusing on complete blood count (CBC) and demographic data. Data mining is the key to building models that help healthcare professionals diagnose and treat anemia. Employing the cross-industry standard process for data mining (CRISP-DM), with its six phases, facilitates this effort. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner tools, evaluating precision, mean recall, and mean precision. The J48 decision tree outperformed the others, highlighting the importance of algorithm choice in anemia classification models. Furthermore, our analysis identified renal disease-related and chronic anemia as the most prevalent types, with greater occurrence among females. Recognizing gender disparities in the prevalence of anemia informs customized healthcare decisions. Understanding demographic factors in specific types of anemia is crucial to effective care strategies.

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“…Our framework moves in this direction by providing a data centric way of analyzing the complex connections between heterogeneous lab experiments and finding the common patterns that can be further investigated in the lab. Several data mining experiments in hematology and other biochemistry based experiments are inclined towards predicting the disease outcome [11,12]. Our study not only uses a predictive model, but it also looks in depth at the features of the in-vitro data individually and in combination to determine which features influence the target variable such as clot formation.…”

Section: Iintroductionmentioning

confidence: 99%

Computational Design Map for Heterogeneous Experimental Studies

Kulkarni

Maisha

et al. 2021

Preprint

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This paper focuses on the discovery of a computational design map of disparate heterogeneous outcomes from bioinformatics experiments in pig (porcine) studies to help identify key variables impacting the experiment outcomes. Specifically we aim to connect discoveries from disparate laboratory experimentation in the area of trauma, blood loss and blood clotting using data science methods in a collaborative ensemble setting. Trauma related grave injuries cause exsanguination and death, constituting up to 50% of deaths especially in the armed forces. Restricting blood loss in such scenarios usually requires the presence of first responders, which is not feasible in certain cases. Moreover, a traumatic event may lead to a cytokine storm, reflected in the cytokine variables. Hemostatic nanoparticles have been developed to tackle these kinds of situations of trauma and blood loss. This paper highlights a collaborative effort of using data science methods in evaluating the outcomes from a lab study to further understand the efficacy of the nanoparticles. An intravenous administration of hemostatic nanoparticles was executed in pigs that had to undergo hemorrhagic shock and blood loss and other immune response variables, cytokine response variables are measured. Thus, through various hemostatic nanoparticles used in the intervention, multiple data outcomes are produced and it becomes critical to understand which nanoparticles are critical and what variables are key to study further variations in the lab. We propose a collaborative data mining framework which combines the results from multiple data mining methods to discover impactful features. We used frequent patterns observed in the data from these experiments. We further validate the connections between these frequent rules by comparing the results with decision trees and feature ranking. Both the frequent patterns and the decision trees help us identify the critical variables that stand out in the lab studies and need further validation and follow up in future studies. The outcomes from the data mining methods help produce a computational design map of the experimental results. Our preliminary results from such a computational design map provided insights in determining which features can help in designing the most effective hemostatic nanoparticles.

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Classification of Hematological Data Using Data Mining Technique to Predict Diseases

Cited by 9 publications

References 13 publications

An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

An Ensemble Models for the Prediction of Sickle Cell Disease from Erythrocytes Smears

Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Computational Design Map for Heterogeneous Experimental Studies

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