What Does Your Tongue Say About You?
Creators: name: Rahman, Ashiqur
Publisher: name: University of Lincoln
Year: 2024
Subjects: G900 - Others in mathematical sciences

description: Diabetes Mellitus, known as Diabetes, is a prevalent chronic condition requires monitoring thehealth condition of a Diabetic patient. Early identification of Diabetes Mellitus can help reducethe risk of an individual to develop devastating complications and can delay or prevent theDiabetes Mellitus condition. To avoid organ related health issues, a Diabetic patient monitorsblood glucose level and checks visible abnormalities on various human organs such eye, tongue,mouth, foot, etc. But to check blood glucose level, a patient needs to take a blood sample forthe conventional medical test that is unpleasant, costly and challenging for an elderly individualwho may need help from another person. The portable smart device technology could enhancethe experience for Diabetic patients to screen and check health condition in their convenience.Oral Candidiasis, Fissured, Glossitis and Geographic abnormality appear on tongue or tonguesurface because of uncontrolled or controlled blood glucose level in a human body and thesetongue abnormalities may appear during early onset of Diabetes Mellitus. A person with Dia?betes Mellitus condition is more likely to develop one or more abnormalities due to high glucoselevel present in the blood. The medical test to measure blood glucose level is inconvenient forpatients living in remote areas where medical services are minimal or not available.This thesis introduces ‘Diatonash’ database, a new Diabetic tongue database comprises Dia?betes Mellitus related clinical test results, digital images of four tongue abnormalities, varioussigns and symptoms, and clinical diagnosis outcomes of secondary diseases complied with west?ern medicine in practice. A streamlined, coherent and efficient clinical diagnosis procedureshave been developed and introduced to screen and diagnose the Diabetic tongue abnormalitiescomplying with western medicine in practice. The new clinical diagnosis procedures (also knownas systemic clinical examination procedures) consider two categories of tongue features such asdistinctive features and shared features of the Diabetic tongue abnormalities. Then, the visualfeatures on tongue extracted manually from 572 digital tongue images of 166 patients followingthe new coherent diagnosis procedures.To screen and check human tongue condition, the proposed computer-aided diagnosis modelclassifies and predicts abnormalities appear on tongue or tongue surface considering visual signscause by Diabetes Mellitus. The Naïve Bayes and Random Forest classifier applied on manuallyextracted visible tongue features to train and construct the computer-aided diagnosis models.Four single models for the four Diabetic tongue abnormalities and one single model for thesame four abnormalities were developed to classify and predict Diabetic tongue abnormalities.The accuracy of the four single machine learning models range from 95.8% to 100% and accuracyof the single machine learning model is 97%. One of the key findings is Random Forest algorithmperformed better than Naïve Bayes algorithm to classify four Diabetic tongue abnormalitiessimultaneously. The proposed computer-aided diagnosis model is convenient to screen and checkDiabetes Mellitus related visible health issues reducing cost for the medical test and accessibleto patients residing in the remote areas.