Azian Azamimi Abdullah
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Preferred name
Azian Azamimi Abdullah
Official Name
Azian Azamimi, Abdullah
Alternative Name
Abdullah, Azian Azamimi
Abdullah, A. A.
Main Affiliation
Scopus Author ID
36968589600
Researcher ID
F-5186-2010

Research Output
Now showing 1 - 5 of 5
  • Publication
    Performance Comparison of Machine Learning Algorithms for Classification of Chronic Kidney Disease (CKD)
    ( 2020-06-17)
    Azian Azamimi Abdullah
    ;
    Hafidz S.A.
    ;
    Wan Khairunizam Wan Ahmad
    Kidney is one of the vital organs in a human body while ironically, chronic kidney disease (CKD) is one of the main causes of death in the world. Due to the low rate of loss of kidney function, the disease is often overlooked until it is in a really bad condition. Dysfunctional kidney may lead to accumulation of wastes in blood which would affect several other systems and functions of the body such as blood pressure, red blood cell production, vitamin D and bone health. Machine learning algorithms can help in classifying the patients who have CKD or not. Even though several studies have been made to classify CKD on patients using machine-learning tool, not many researchers perform pre-processing and feature selection technique to obtain quality and dependable result. Machine learning used with feature selection techniques are shown to have better and more dependable result. In this study, feature selection methods such as Random Forest feature selection, forward selection, forward exhaustive selection, backward selection and backward exhaustive selection were identified and evaluated. Then, machine learning classifiers such as Random Forest, Linear and Radial SVM, Naïve Bayes and Logistic Regression were implemented. Lastly, the performance of each machine-learning model was evaluated in terms of accuracy, sensitivity, specificity and AUC score. The results showed that Random Forest classifier with Random Forest feature selection is the most suitable machine learning model for classification of CKD as it has the highest accuracy, sensitivity, specificity and AUC with 98.825%, 98.04%, 100% and 98.9% respectively which outperformed other classifiers.
  • Publication
    Development of Computer Aided System for Classification of Gastrointestinal Lesions
    ( 2022-01-01)
    Azian Azamimi Abdullah
    ;
    Kamardin N.A.A.
    ;
    Wan Khairunizam Wan Ahmad
    Colorectal cancer is a major global health problem and is one of the major contributors to deaths worldwide. Because malignant transformations are rare, endoscopic resection of hyperplastic polyps exacerbates medical costs, including those for resection and unnecessary pathological assessment. The proposed work is based on assessment of exploratory data analysis and visualization, initial pre-processing step followed by selection of attributes and performance assessment of proposed supervised machine learning algorithms. For features selection, two method were implemented (Boruta and SelectFromModel (SFM) Random Forest) to compare the performances of the models. The comparative analysis of machine learning included Random Forest (RF), Support Vector Machines (SVM), Deep Neural Network (DNN). For Boruta algorithm, it is shown that RF has the highest accuracy of 90.32%, sensitivity of 93.05% and specificity of 95.95%. Therefore, the adenoma detection rate (ADR) is desirable for improvement. A computer-aided system is developed which offers the opportunity to evaluate the presence of colorectal polyps objectively during colonoscopy.
  • Publication
    Detection of Parkinson's Disease (PD) Based on Speech Recordings using Machine Learning Techniques
    ( 2020-12-20)
    Azian Azamimi Abdullah
    ;
    Norazman N.N.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Saidatul Ardeenawatie Awang
    ;
    Jian F.W.
    There are some neurodegenerative diseases which are unable to cure such as Parkinson's disease (PD) and Hungtinton's disease due to the death of certain parts in the brain that is affecting older adult. PD is an appalling neurodegenerative health disorder that linked to the nervous system which exert influence on motor functions. PD also often known as idiopathic disorder, environmental and genetic factors related, and the causes of PD remain unidentified. To diagnose PD, the clinicians are required to take the history of brain condition for the patient and undergoes various of motor skills examination. Accurate detection of PD plays a crucial role in aiding and providing proper treatment to the patients. Nowadays, there has been recent interest in studying speech-based PD diagnosis. Extracted acoustic attributes are the most important requirement to predict the PD. The experiment was conducted on speech recording dataset consisting of 240 samples. This work studies on the feature selection method, Least Absolute Shrinkage and Selection Operator (LASSO) with multiple machine learnings such as Random Forest (RF), Deep Neural Network (DNN), Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) as the classifier. Throughout this research, train test split method and k-fold cross validation were implemented to evaluate the performance of the classifiers. Through LASSO, Support Vector Machine Grid Search Cross Validation (SVM GSCV) outperformed other 7 models with 100.00 % accuracy, 97.87 % for recall, 65.00 % for specificity and 97.10 % of AUC for 10-fold cross validation. Finally, Graphical User Interface (GUI) was developed and validated through the prediction over UCI speech recording dataset which achieved 96.67 % accuracy for binary classification with 30 samples.
  • Publication
    Detection of Parkinson’s Disease (PD) based on speech recordings using machine learning techniques
    ( 2020)
    Azian Azamimi Abdullah
    ;
    Nurul Nurain Norazman
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Saidatul Ardeenawatie Awang
    ;
    Foong Wei Jian
    There are some neurodegenerative diseases which are unable to cure such as Parkinson's disease (PD) and Hungtinton's disease due to the death of certain parts in the brain that is affecting older adult. PD is an appalling neurodegenerative health disorder that linked to the nervous system which exert influence on motor functions. PD also often known as idiopathic disorder, environmental and genetic factors related, and the causes of PD remain unidentified. To diagnose PD, the clinicians are required to take the history of brain condition for the patient and undergoes various of motor skills examination. Accurate detection of PD plays a crucial role in aiding and providing proper treatment to the patients. Nowadays, there has been recent interest in studying speech-based PD diagnosis. Extracted acoustic attributes are the most important requirement to predict the PD. The experiment was conducted on speech recording dataset consisting of 240 samples. This work studies on the feature selection method, Least Absolute Shrinkage and Selection Operator (LASSO) with multiple machine learnings such as Random Forest (RF), Deep Neural Network (DNN), Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) as the classifier. Throughout this research, train test split method and k-fold cross validation were implemented to evaluate the performance of the classifiers. Through LASSO, Support Vector Machine Grid Search Cross Validation (SVM GSCV) outperformed other 7 models with 100.00 % accuracy, 97.87 % for recall, 65.00 % for specificity and 97.10 % of AUC for 10-fold cross validation. Finally, Graphical User Interface (GUI) was developed and validated through the prediction over UCI speech recording dataset which achieved 96.67 % accuracy for binary classification with 30 samples.
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  • Publication
    Diagnosis of Heart Disease Using Machine Learning Methods
    ( 2021-01-01)
    Azian Azamimi Abdullah
    ;
    Alhadi N.A.
    ;
    Wan Khairunizam Wan Ahmad
    The World Health Organization (WHO) estimated 12 million deaths around the world appear each year from heart disease. Heart disease includes coronary artery disease, heart rhythm problem and heart defects. Each disease has similar symptoms but cause different effects and severity on patient. The common factors of heart disease include high blood pressure, diabetes, cholesterol and age. These factors are independent of each other; thus, the use of artificial intelligence and machine learning will be a suitable choice to model them. Correct diagnosis of heart disease is difficult due to the complicated processes and different system and it is vital because heart disease can lead to a heart attack, chest pain, stroke and sudden death. Hence, an accurate and early detection of heart disease with proper and adequate treatment is needed. The main aim of this research is to identify suitable feature selection method and machine learning algorithms for the diagnosis of heart disease. Chi Square Feature Selection (CSFS), Random Forest Feature Selection (RFFS), Forward Feature Selection (FFS), Backward Feature Selection (BFS) and Exhaustive Feature Selection (EFS) are the feature selection methods applied in this research. These feature selection methods are then implemented in the machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k-Nearest Neighbour (KNN). The performance of these machine learning algorithms is evaluated through accuracy, sensitivity and specificity based on Confusion Matrix, ROC Curve and area under ROC (AUC). Based on the results, combination of RF with RFFS produced the highest accuracy value with 85.25% accuracy.
      2  21