The correlation between oxidative stress markers and increasing intracranial pressure: a study of Malondialdehyde (MDA), Superoxide Dismutase (MnSOD), Nicotinamide Adenine Dinucleotide Phosphate Hydrogen (NADPH), and catalase Wismaji Sadewo, Septelia Inawati Wanandi, Zuherman Rustam, Kevin Gunawan, Setyo Widi Nugroho Bali Medical Journal, 2023 Link of Video Abstract: https://youtu.be/nINFPi4uVow Background: Abnormalities in the neurosurgical field concerning cranial structure are almost always followed by increased intracranial pressure (ICP), which causes cerebral hypoxia and disrupts the whole cellular system. This study analyzes the correlation between ICP changes and oxidative stress markers, in subjects with various anatomical pathology abnormalities who underwent surgery. Methods: This was a cross sectional study of 24 subjects with ICP conducted in the Department of Neurosurgery, Cipto Mangunkusumo Hospital, Jakarta, from November to December 2009. In this study, ANOVA analysis has been done to measure the mean difference oxidative stress (MnSOD, NADPH, MDA, and catalase) in subjects with various changes in ICP who have undergone surgery and Spearman analysis to measure the correlation between each oxidative stress markers. Oxidative stress markers were measured from normal brain tissue taken intra-operatively, cerebrospinal fluid (CSF) taken while doing ventriculostomy, and peripheral blood taken from a central vein catheter (CVC). Results: The results showed that MDA concentrations in blood were significantly correlated with MDA in brain tissue (P = 0.029) in all of the ICP groups, CSF MnSOD concentration and CSF NADPH concentrations correlated with brain tissue NAPDH (p= 0.000) in congenital group and CSF catalase concentration correlated with brain tissue catalase (p = 0.00) in congenital group. Conclusion: Increased ICP caused by various pathological conditions causes changes in oxidative stress markers in brain cells, CSF and blood. Oxidative stress markers in blood and CSF are correlated strongly with the brain. It is suggested that it could be used to predict intracranial high pressure in all cases with intracranial abnormalities.
An Efficient and Robust Ischemic Stroke Detection Using a Combination of Convolutional Neural Network (CNN) and Kernel KMeans Clustering Zuherman Rustam, Sri Hartini, Fevi Novkaniza, Jacob Pandelaki, Rahmat Hidayat, Mostafa Ezziyyani International Journal on Advanced Science Engineering and Information Technology, 2023 This study introduces a combined approach utilizing the widely-used Convolutional Neural Network (CNN) and Kernel K-Means clustering method for the detection of ischemic stroke from Magnetic Resonance Imaging (MRI) images. We propose an efficient and robust alternating classification scheme to overcome the challenges of extensive computation time and noisy ischemic stroke images obtained from Cipto Mangunkusumo Hospital in Indonesia. The method incorporates multiple convolutional layers from the CNN architecture and subsequently vectorizes the matrix output to serve as input for Kernel K-Means clustering. Through a series of experiments, our proposed method has demonstrated highly promising results. Employing 11-fold cross-validation and the RBF kernel function (sigma= 0.05), we achieved exceptional performance metrics, including 99% accuracy, 100% sensitivity, 98% precision, 98.04% specificity, and 98.99% F1-Score. These outcomes underscore the remarkable capabilities of the combined CNN and Kernel K-Means clustering approach in accurately identifying ischemic stroke cases. Furthermore, our method exhibits competitive performance when compared to several other state-of-the-art methods in the field of deep learning. By harnessing the power of CNN's convolutional layers and the clustering capability of Kernel K-Means, we have achieved significant advancements in the domain of ischemic stroke detection from MRI images. The implications of this research are substantial. By enhancing the accuracy and efficiency of ischemic stroke detection, our method has the potential to assist medical professionals in making timely and informed decisions for stroke patients. Early detection and intervention can greatly improve patient outcomes and contribute to more effective treatment strategies.
An Analysis of Several Optimizers on CNNSVM and CNNRF for COVID–19 Chest X–ray Images Zuherman Rustam, Jane Eva Aurelia, Fevi Novkaniza, Sri Hartini, Rahmat Hidayat, Mostafa Ezziyyani International Journal on Advanced Science Engineering and Information Technology, 2023 COVID-19 is a new type of ailment caused by the strenuous acute respiratory syndrome, namely SARS-CoV-2, also frequently well-known as the Coronavirus. An early tendency of COVID-19 for some sufferers can cause no symptoms at all as no experience is referred to as asymptomatic confirmation cases, yet these sufferers can still transmit COVID-19 to other people. Therefore, the authors developed a program using Machine Learning that sustains data to be analyzed based on the input served under the proposed methods of Convolutional Neural Network-Support Vector Machine (CNNSVM) and Convolutional Neural Network-Random Forest(CNNRF), along with several optimizers to be compared. Convolutional Neural Networks is a deep learning algorithm that can train large data sets with millions of parameters and has attracted attention in various fields that are commonly used for the classification and detection of Convolution in Neural Networks. In amalgamation with Support Vector Machines, a technique that uses two vectors to form a dividing line or side and fairly high accuracy,y random forests classification. In the manner of image data obtained from ChestX-ray images of people with COVID-19 from the Italian Society of Medical and Interventional Radiology (SIRM), a total of 1750 observations consisting of 1000 data for COVID¬-19 images and 750 data for non-COVID-19 images. This research aims to determine which optimizer is better for analyzing COVID-19 ChestX-ray images by evaluating both methods. Hopefully, both methods can provide higher accuracy for future studies with wider databases to provide better results for analyzing different ailments.
Designing Hybrid CNN-SVM Model for COVID-19 Classification Based on X-ray Images Using LGBM Feature Selection Sri Hartini, Zuherman Rustam, Rahmat Hidayat International Journal on Advanced Science Engineering and Information Technology, 2022 COVID-19 still exists at an alarming level; hence, early diagnosis is important for treating and controlling this disease due to its rapid spread. The use of X-rays in medical image analysis can play an essential role in fast and affordable diagnosis. This study used a two-level feature selection in hybrid deep convolutional features obtained from the extraction of X-ray images. The transfer learning-based approach was implemented using five convolutional neural networks (CNNs) named VGG16, VGG19, ResNet50, InceptionV3, and Xception. The combination of two or three CNNs' performance as a feature extractor was then carefully analyzed. We selected the features obtained from multiple CNNs in a particular layer with a specified percentage of features in the first level for getting relevant features from various models. Then, we combined those features and did the second level of feature selection to select the most informative features. Both levels of feature selection were carried out using the light gradient boosting machine (LightGBM) algorithm. The final feature set has been used to classify COVID-19 and non-COVID-19 chest X-ray images using the support vector machines (SVM) classifier. The proposed model's performance was evaluated and analyzed on the open-access dataset. The highest accuracy was 99.80% using only 5% of the features extracted from ResNet50 and Xception. The other way of combining the ensemble of deep features and a few recent works for the classification of COVID-19 were also compared with the proposed model. As a result, our proposed model has achieved the best success rate for this dataset and may be deployed to support decision systems for radiologists.
Twin support vector machine using kernel function for colorectal cancer detection Zuherman Rustam, Fildzah Zhafarina, Jane Eva Aurelia, Yasirly Amalia Bulletin of Electrical Engineering and Informatics, 2021 Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
Twin Support Vector Machines for Thalassemia Classification Alva Andhika Sa'id, Zuherman Rustam, Fevi Novkaniza, Qisthina Syifa Setiawan, Faisa Maulidina, Velery Virgina Putri Wibowo 2021 International Conference on Innovation and Intelligence for Informatics Computing and Technologies 3ict 2021, 2021 Thalassemia is one of the incurable blood disorders inherited from parents with its history. This disease causes abnormality in the blood cells, specifically the protein composition such as hemoglobin. Furthermore, it has spread out across the Mediterranean Sea and through Indonesia due to the migration of people. Early detection to diagnose thalassemia is necessary to prevent the disease from spreading to another generation. This study aims to analyze the impact of machine learning in medical diagnosis, and its disease detection methods based on clinical history. Several previous studies have been incorporated into early screening for diagnosis of thalassemia with machine learning technique based on classification problem, and it showed great performance evaluation beyond 90% accuracy. In addition, the data used was laboratory results of blood check obtained from Harapan Kita Children and Women's Hospital, Jakarta, Indonesia. Twin Support Vector Machines (TSVM) is used in this study as one of the machine learning developed techniques inspired by Support Vector Machines (SVM), as this technique purposed to find the nonparallel hyperplanes to solve binary classification problem. This was conducted through three commonly used kernels from several previous studies, including Linear, Polynomial, and Radial Basis Function (RBF). The results showed that RBF TSVM gave the best results with 99.32%, 99.75% and 99.24% average of accuracy, precision, and F1 score, respectively. However, Polynomial TSVM, as the lowest results had 99.79% average of recall. In this context, the TSVM role is recommended for future studies to facilitate medical diagnosis based on the clinical history of other diseases.
Lung cancer classification using fuzzy C-means and fuzzy Kernel C-means based on CT scan image Zuherman Rustam, Aldi Purwanto, Sri Hartini, Glori Stephani Saragih Iaes International Journal of Artificial Intelligence, 2021 <span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>
Comparison support vector machines and K-nearest neighbors in classifying Ischemic stroke by using convolutional neural networks as a feature extraction Glori Saragih, Zuherman Rustam ACM International Conference Proceeding Series, 2021 The paper introduces the hybrid method of Convolutional Neural Network (CNN) and machine learning methods as a classifier, that is Support Vector Machines and K-Nearest Neighbors for classifying the ischemic stroke based on CT scan images. CNN is used as a feature extraction and the machine learning methods used to replace the fully connected layers in CNN. The proposed method is used to reduce computation time and improve accuracy in classifying image data, because we know that deep learning is not efficient for small amounts of data, where the data we use is only 93 CT scan images obtained from Cipto Mangunkusumo General Hospital (RSCM), Indonesia. The architecture of CNN used in this research consists of 5 layers convolutional layers, ReLU, MaxPooling, batch normalization and dropout. The elapsed time required for CNN is 7.631490 seconds. The output of feature extraction is used as an input for SVM and KNN. SVM with linear kernel can correctly classify ischemic stroke, with 100% accuracy in the training model and 96% accuracy in testing model with a test size of 60%. KNN classify ischemic stroke, with 97.3% (#neighbors = 5) accuracy in training model with a test size of 60% and 90% (#neighbors = 10, 15, 25) accuracy in the testing model with a test size of 10%. Based on these results, the SVM produces the higher accuracy compared to KNN in classifying ischemic stroke using CNN as feature extraction based on CT scan images with a computation time of only 8.0973 seconds.
An approach for COVID-19 detection using deep convolutional features on chest X-ray images Journal of Theoretical and Applied Information Technology, 2021
Comparison of Colorectal Cancer Classification between K-Nearest Neighbors (K-NN) and Neural Network F Zhafarina, Z Rustam, Y Amalia, I Wirasati Journal of Physics Conference Series, 2021 Abstract Machine learning is one of the technologies used in medicine. Machine learning can help detect various kinds of problems in the medical field and enables a process to be faster and more efficient. Cancer is one of the most dangerous diseases in the world. Machine learning is widely used in bioinformatics and particularly in cancer diagnosis. One of the most popular methods is K-nearest neighbors (K-NN) and Neural Network. There are supervised learning methods. Using K-NN, the quality of the results depends largely on the distance and the value of the parameter “k” which represents the number of the nearest neighbors. This research is explains the classification of colorectal cancer by using K-NN with different k values and Neural Network Classification. Our work will be performed on the Colorectal Cancer dataset obtained by the Al-Islam Hospital, Bandung, Indonesia and it consists of benign cases 163 and malignant cases 47 samples. Thus, the final result indicates better performance for K-nearest neighbors’ accuracy is 0.786 in K-parameter equal to 7, 9, 11 has the same accuracy with 60% data training and Neural Network reached 0.904 with 90% of data training.
Fuzzy C-Means-Grey Wolf Optimization for Classification of Stroke Qisthina Syifa Setiawan, Zuherman Rustam, Alva Andhika Sa'id, Faisa Maulidina, Wismaji Sadewo, Fevi Novkaniza 2021 International Conference on Decision Aid Sciences and Application Dasa 2021, 2021
Application of support vector regression for Jakarta stock composite index prediction with feature selection using laplacian score Journal of Theoretical and Applied Information Technology, 2019
Clustering Arrhythmia Multiclass Using Fuzzy Robust Kernel C-Means (FRKCM) Nedya Shandri, Zuherman Rustam Proceedings of Icaiti 2018 1st International Conference on Applied Information Technology and Innovation Toward A New Paradigm for the Design of Assistive Technology in Smart Home Care, 2018
Cancer classification using Fuzzy C-Means with feature selection Arvan Aulia Rachman, Zuherman Rustam Proceedings 2016 12th International Conference on Mathematics Statistics and their Applications Icmsa 2016 in Conjunction with the 6th Annual International Conference of Syiah Kuala University, 2017
Application Kernel Modified Fuzzy C-Means for gliomatosis cerebri Andi Wulan, Melati Vidi Jannati, Zuherman Rustam, Ahmad Afif Fauzan Proceedings 2016 12th International Conference on Mathematics Statistics and their Applications Icmsa 2016 in Conjunction with the 6th Annual International Conference of Syiah Kuala University, 2017
Fuzzy kernel C-means algorithm for intrusion detection systems Journal of Theoretical and Applied Information Technology, 2015
Fuzzy kernel K-medoids algorithm for multiclass multidimensional data classification Journal of Theoretical and Applied Information Technology, 2015
Non-invasive intracranial pressure classification using strong jumping emerging patterns Icacsis 2011 2011 International Conference on Advanced Computer Science and Information Systems Proceedings, 2011
An initialization scheme of fuzzy-neuro LVQ for discriminating three-mixtures odor Proceedings of the 5th IASTED International Conference on Signal Processing Pattern Recognition and Applications Sppra 2008, 2008
