Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system

Çil, Betül; Ayyıldız, Hakan; Tuncer, Taner

doi:10.1016/j.mehy.2020.109611

Cited by 40 publications

(16 citation statements)

References 20 publications

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“…By using such details, proper medicine can be prescribed to the right person. Authors concluded that the BMI (Biomedical Information) is the best choice for the improvement of the individuals health especially β-Thalassemia patients Cil et al [30] proposed a model for the differentiation of β-Thalassemia patients from the IDA patients because the symptoms of both the patients are almost similar. The screening of both diseases is also important because if both are misdiagnosed or confused with one another it would lead to serious complications.…”

Section: Related Workmentioning

confidence: 99%

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

et al. 2021

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Thalassemia is viewed as a prevalent inherited blood disease that has gotten exorbitant consideration in the field of medical research around the world. Inherited diseases have a high risk that children will get these diseases from their parents. If both the parents are β-Thalassemia carriers then there are 25% chances that each child will have β-Thalassemia intermediate or β-Thalassemia major, which in most of its cases leads to death. Prenatal screening after counseling of couples is an effective way to control β-Thalassemia. Generally, identification of the Thalassemia carriers is performed by some quantifiable blood traits determined effectively by high-performance-liquid-chromatography (HPLC) test, which is costly, time-consuming, and requires specialized equipment. However, cost-effective and rapid screening techniques need to be devised for this problem. This study aims to detect β-Thalassemia carriers by evaluating red blood cell indices from the complete-blood-count test. The present study included Punjab Thalassemia Prevention Project Lab Reports dataset. The proposed SGR-VC is an ensemble of three machine learning algorithms: Support Vector Machine, Gradient Boosting Machine, and Random Forest. Comparative analysis proved that the proposed ensemble model using all indices of red blood cells is very effective in β-Thalassemia carrier screening with 93% accuracy.

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Section: Related Workmentioning

confidence: 99%

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

et al. 2021

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“…They performed a 95.59% successful classification using logistic regression, KNN, SVM, ELM, and RELM algorithms. They used HGB, RBC, HCT, MCV, MCH, MCHC, and RDW parameters of 342 patients [36]. Varghese conducted a study highlighting the importance of machine learning methods in the classification of blood cells and blood diseases.…”

Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

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Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

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“…Due to its excellent performance in classification and regression, the ELM has attracted the attention of scholars from multiple directions. [14][15][16][17][18] Compared with BP neural network, the ELM does not need to adjust the weights between layers of neurons in the training process to obtain the unique optimal solution, thus greatly saving the operation time of the algorithm. This helps find out the potential customers quickly and take relevant measures to retain them in case of customer churn.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the extreme learning machine (ELM) has the advantage of economy and speed in forecasting classification. Due to its excellent performance in classification and regression, the ELM has attracted the attention of scholars from multiple directions 14–18 . Compared with BP neural network, the ELM does not need to adjust the weights between layers of neurons in the training process to obtain the unique optimal solution, thus greatly saving the operation time of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

An early warning model for customer churn prediction in telecommunication sector based on improved bat algorithm to optimize ELM

Chun

Liu

et al. 2021

Int J Intell Syst

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The competition in the telecommunications industry is mainly reflected in the competition for the number of key customers. Therefore, how to effectively prevent the loss of large customers is a problem that many telecommunications companies are very concerned about. This paper establishes a customer churn prediction model based on the improved bat algorithm (IBA) optimized extreme learning machine (ELM), with the purpose of discovering potential churn customers in time and taking measures to retain them in advance. This paper uses the IBA to optimize the initial random weights of the ELM, so as to improve the prediction accuracy of the ELM. To overcome the shortcomings of the bat algorithm (BA) that the convergence speed is too slow in the early stage and difficult to converge in the later stage, this paper introduces the inertia weight into the speed update formula. To improve the population diversity and local search ability of the BA, this paper adds a chaotic local search method to balance the global search ability of the BA in the early stage. To control the search range of the BAs in the later stage, the lane flight formula is introduced into the position update formula to speed up the convergence speed of the algorithm. Then according to the result of function optimization, it can be seen that IBA has a significant improvement in convergence speed and accuracy. Finally, IBA was used to optimize ELM, and a customer churn prediction model was established. The empirical results show that the predictive model proposed in this paper can effectively identify lost customers and lay a foundation for improving the competitiveness of the telecommunications industry in the future.

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Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system

Cited by 40 publications

References 20 publications

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

Diagnosing Hematological Disorders Using Deep Learning Method

An early warning model for customer churn prediction in telecommunication sector based on improved bat algorithm to optimize ELM

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