Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Aida Mustapha
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Comparative analysis of classification algorithms for chronic kidney disease diagnosis Zainuri Saringat; Aida Mustapha; R. D. Rohmat Saedudin; Noor Azah Samsudin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.3 KB) | DOI: 10.11591/eei.v8i4.1621

Abstract

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.
Comparative analysis on bayesian classification for breast cancer problem Wan Nor Liyana Wan Hassan Ibeni; Mohd Zaki Mohd Salikon; Aida Mustapha; Saiful Adli Daud; Mohd Najib Mohd Salleh
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (585.991 KB) | DOI: 10.11591/eei.v8i4.1628

Abstract

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.
Co-Authors Abdullah Baz Adeleke Abdullahi Adillah Dayana Ahmad Dali Ali Hashim Abbas Azizan Ismail Azizi Abas Chan Kok Fu Farooq Sijal Shaqwi Fawwaz Zamir Mansor Filipe Manuel Clemente Hairulnizam Mahdin Hannani Aman Lau Tian Rui Lim Mei Shi Luqman Hanif Audah Marshella Hopman Md fauzi Ahmad Mohamad Aizi Salamat Mohamad Firdaus Che Abdul Rani Mohamad Izzuddin Rahman Mohammad Faidzul Nasrudin Mohammed Ahmed Jubair Mohd Hisyam Abdul Rahim Mohd Najib Mohd Salleh Mohd Zaki Mohd Salikon Mustafa Hamid Hassan Nazim Razali Noor Azah Samsudin Nor Azlina Abd Rahman Nur Haryani Zakaria Nurul Aswa Omar Raihan Sabirah Sabri Rd. Rohmat Saedudin Roshidi Din Rosmadi Bakar Sadesh Manikam Saiful Adli Daud Salama A. Mostafa Sara Afiqah Mohd Zailani Shihab Hamad Khaleefah Shuhaida Ismail Siti Norussaadah Mohd Salleh Sonia Siow Suhaibah Jusoh Sunariya Utama Syiham Mohd Lokman Tan Sau Tong Umar Farooq Vinothini Kasinathan Wan Nor Liyana Wan Hassan Ibeni Yana Mazwin Mohmad Hassim Zainuri Saringat Zehan Afizah Afif