{"title":"机器学习模型可以预测血红蛋白变异的存在:基于人工神经网络的β-地中海贫血和缺铁性贫血的识别","authors":"Süheyl Uçucu, Fatih Azik","doi":"10.5937/jomb0-38779","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data.</p><p><strong>Methods: </strong>This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections.</p>","PeriodicalId":16175,"journal":{"name":"Journal of Medical Biochemistry","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943455/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence-driven diagnosis of β-thalassemia minor & iron deficiency anemia using machine learning models.\",\"authors\":\"Süheyl Uçucu, Fatih Azik\",\"doi\":\"10.5937/jomb0-38779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data.</p><p><strong>Methods: </strong>This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections.</p>\",\"PeriodicalId\":16175,\"journal\":{\"name\":\"Journal of Medical Biochemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943455/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Biochemistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5937/jomb0-38779\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Biochemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5937/jomb0-38779","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Artificial intelligence-driven diagnosis of β-thalassemia minor & iron deficiency anemia using machine learning models.
Background: Iron deficiency anemia (IDA) and b-thalassemia minor (BTM) are the two most common causes of microcytic anemia, and although these conditions do not share many symptoms, differential diagnosis by blood tests is a time-consuming and expensive process. CBC can be used to diagnose anemia, but without advanced techniques, it cannot differentiate between iron deficiency anemia and BTM. This makes the differential diagnosis of IDA and BTM costly, as it requires advanced techniques to differentiate between the two conditions. This study aims to develop a model to differentiate IDA from BTM using an automated machine-learning method using only CBC data.
Methods: This retrospective study included 396 individuals, consisting of 216 IDAs and 180 BTMs. The work was divided into three parts. The first section focused on the individual effects of hematological parameters on the differentiation of IDA and BTM. The second part discusses traditional methods and discriminant indices used in diagnosis. In the third section, models developed using artificial neural networks (ANN) and decision trees are analysed and compared with the methods used in the first two sections.
期刊介绍:
The JOURNAL OF MEDICAL BIOCHEMISTRY (J MED BIOCHEM) is the official journal of the Society of Medical Biochemists of Serbia with international peer-review. Papers are independently reviewed by at least two reviewers selected by the Editors as Blind Peer Reviews. The Journal of Medical Biochemistry is published quarterly.
The Journal publishes original scientific and specialized articles on all aspects of
clinical and medical biochemistry,
molecular medicine,
clinical hematology and coagulation,
clinical immunology and autoimmunity,
clinical microbiology,
virology,
clinical genomics and molecular biology,
genetic epidemiology,
drug measurement,
evaluation of diagnostic markers,
new reagents and laboratory equipment,
reference materials and methods,
reference values,
laboratory organization,
automation,
quality control,
clinical metrology,
all related scientific disciplines where chemistry, biochemistry, molecular biology and immunochemistry deal with the study of normal and pathologic processes in human beings.