Intelligent Data Analysis - Volume 27, issue S1

Article Type: Editorial

DOI: 10.3233/IDA-239006

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 1-2, 2023

Authors: Prasanna, K. | Jyothi, Chinna Babu | Mathivanan, Sandeep Kumar | Jayagopal, Prabhu | Saif, Abdu | Samuel, Dinesh Jackson

Article Type: Research Article

Abstract: Cephalometric analysis is used to identify problems in the development of the skull, evaluate their treatment, and plan for possible surgical interventions. The paper aims to develop a Convolutional Neural Network that will analyze the head position on an X-ray image. It takes place in such a way that it recognizes whether the image is suitable and, if not, suggests a change in the position of the head for correction. This paper addresses the exact rotation of the head with a change in the range of a few degrees of rotation. The objective is to predict the correct head position …to take an X-ray image for further Cephalometric analysis. The changes in the degree of rotations were categorized into 5 classes. Deep learning models predict the correct head position for Cephalometric analysis. An X-ray image dataset on the head is generated using CT scan images. The generated images are categorized into 5 classes based on a few degrees of rotations. A set of four deep-learning models were then used to generate the generated X-Ray images for analysis. This research work makes use of four CNN-based networks. These networks are trained on a dataset to predict the accurate head position on generated X-Ray images for analysis. Two networks of VGG-Net, one is the U-Net and the last is of the ResNet type. The experimental analysis ascertains that VGG-4 outperformed the VGG-3, U-Net, and ResNet in estimating the head position to take an X-ray on a test dataset with a measured accuracy of 98%. It is due to the incorrectly classified images are classified that are directly adjacent to the correct ones at intervals and the misclassification rate is significantly reduced. Show more

Keywords: Deep learning, cephalometric analysis, convolution neural network, image recognition, VGG model

DOI: 10.3233/IDA-237430

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 3-27, 2023

Authors: Pavitra, B. | Singh, D. Narendar | Sharma, Sudhir Kumar | Hashmi, Mohammad Farukh

Article Type: Research Article

Abstract: In the last decades the health care developments highly rise the level of ages of world population. This improvement was accompanied by increasing the diseases related with elder like Dementia, which Alzheimer’s disease represents the most common form. The present studies aim to design and implementation a medical system for improving the life of Alzheimer’s disease persons and ease the burden of their caregivers. AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patient’s future health, and recommend better treatments. AI goes beyond the foundations of deep learning to give you insight …into the nuances of applying AI to medical use cases. Diagnosis is about identifying disease. By building an algorithm we can diagnosis chest X-ray and determine whether it contains disease, another algorithm that will look at brain MRIs and identify the location of tumours in those brain MRIs health of the patients lab values and their demographics and use those to predict the risk of an event. A Smart IOT Interactive Assistance is a technological device that continuously monitors the stability of an Alzheimer’s patient, indicates their position on a map, automatically reminds them to take their prescriptions, and has a call button for any emergencies they could experience during the day. The system has two components, one of which the patient wears and the other of which is an IoT platform application utilized by the caregiver. The motion processing unit sensor, GPS, heart rate sensor with microcontrollers, and LCD display were used to construct the wearable device. An Internet of Things (IoT) platform supports this device, allowing the caregiver to communicate with the patient from any location. Show more

Keywords: Dementia prediction, Internet of Things in Medical, architecture, framework, GPS, EEG-EMG signals

DOI: 10.3233/IDA-237431

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 29-45, 2023

Authors: Shervegar, Vishwanath Madhava

Article Type: Research Article

Abstract: OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform …and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy. Show more

Keywords: Phonocardiogram (PCG), Support Vector Machine (SVM), Coronary Artery Disease (CAD), Low-Pass Butterworth Filter (LPBF), WS Transform (WST), Feature Extraction (FE)

DOI: 10.3233/IDA-237432

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 47-63, 2023

Authors: Hassoon, Noor Hasan | Ali, Mohammed Hasan | Jaber, Mustafa Musa | Abd, Sura Khalil | Abosinnee, Ali S. | Kareem, Z.H.

Article Type: Research Article

Abstract: Epilepsy patients who are presently refractory may be monitored using a seizure prediction Brain-Computer Interface (BCI), which uses electrodes strategically implanted in the brain to anticipate and regulate the onset and duration of a seizure. Real-time approaches to these technologies have challenges, as seen by seizures’ instantaneous electrographic activity. Electroencephalographic (EEG) signals are inherently non-stationary, which means that the regular and seizure signals differ significantly among people with epilepsy. Due to the restricted number of contacts on electrodes, dynamically processed and collected characteristics cannot be employed in a prediction function without causing significant processing delays. Big data can guarantee secure …storage in these situations, and it has the maximum processing capability to identify, record, and analyze time in real-time to conduct the seizure event on the timetable. Seizure prediction and location for huge Scalp EEG recordings have been the focus of this study, which used wearable sensor data and deep learning to use cloud storage to develop the systems. A novel technique is suggested to avoid an epileptic seizure and discover the seizure origin from the utilized wearable sensors. Secondly, deep learning architectures called Clustered Autoencoder with Convolutional Neural Network (CAE-CNN), an expanded optimization methodology is presented based on the Principal Component Analysis (PCA), the Hierarchical Searching Algorithm (HSA), and the Medical Internet of Things (MIoT) has been established to define the suggested frameworks based on the collection of big data storage of the wearable sensors in real-time, automatic computation and storage. According to clinical trials, CAE-CNN outperforms the current wearable sensor-based treatment for unresolved chronic epilepsy patients. Show more

Keywords: Chronic epilepsy, big data, MIoT, wearable sensors, seizure prediction, auto encoder, CNN, PCA, HSA

DOI: 10.3233/IDA-237434

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 65-82, 2023

Authors: Baazeem, Rami | Maheshwary, Priti | Binjawhar, Dalal Nasser | Gulati, Kamal | Joshi, Shubham | Ojo, Stephen | Pareek, Piyush Kumar | Shukla, Prashant Kumar

Article Type: Research Article

Abstract: Nanomaterials are finding increasingly diverse medical uses as technology advances. Researchers are constantly being introduced to new and improved methods, and these applications see widespread use for both diagnostic and therapeutic purposes. Early disease detection, efficient drug delivery, cosmetics and health care products, biosensors, miniaturisation techniques, surface improvement in implantable biomaterials, improved nanofibers in medical textiles, etc. are all examples of how biomedical nanotechnology has made a difference in the medical field. The nanoparticles are introduced deliberately for therapeutic purposes or accidentally from the environment; they will eventually reach and penetrate the human body. The exposed nanoparticles interact with human …blood, which carries them to various tissues. An essential aspect of blood rheology in the microcirculation is its malleability. As a result, nanomaterial may cause structural abnormalities in erythrocytes. Echinocyte development is a typical example of an induced morphological alteration. The length of time it takes for these side effects to disappear after taking a nano medication also matters. Haemolyses could result from the dangerous concentration. In this experiment, human blood is exposed to varying concentrations of chosen nanomaterial with potential medical applications. The morphological modifications induced were studied by looking at images of erythrocyte cells. That’s a picture of a cell taken using a digital optical microscope, by the way. We used MATLAB, an image-analysis programme, to study the morphometric features. Human lymphocyte cells were used in the cytotoxic analysis. Show more

Keywords: Nano particles, haemolytic, hemolysis, echinocytes, carbon nanotube

DOI: 10.3233/IDA-237435

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 83-94, 2023

Authors: Karthick, M. | Samuel, Dinesh Jackson | Prakash, B. | Sathyaprakash, P. | Daruvuri, Nandhini | Ali, Mohammed Hasan | Aiswarya, R.S.

Article Type: Research Article

Abstract: This research focused on Real-time MRI lung images that were revealed using three grade processes by manipulating nanophotonics components, mapping by deep learning, machine learning, and pattern recognition. This research is Solving Magnetic resonance imaging of interstitial lung diseases with Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture. The feedforward deep neural network (ffDNN) and Convolutional Neural Network (CNN) techniques are used to Solving Magnetic resonance imaging of interstitial lung diseases on the nanophotonics components, deep learning, and machine learning Platform. The Proposed semiconductor monolithic integration approach employed for bio-Magnetic resonance imaging characterization using photonic crystal …“Symptomatic Image Revealing” details of the resonant monolithic. The proposed machine-learning-based approach revealed characterizing multi-parameter design space of nanophotonic components using Nano-optic imagers. The Pattern Recognition for MRI was performed for lower dimensionality. Finally, the Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture for calculating the height and size of scatterers using the inverse design of the meta-optical structure. The temporal resolution assessment of image data pixel size 280x360 hyperspectral imaging temporal resolution is 25, and magnetic resonance imaging temporal resolution is 50. The Image distribution shows that phase shift and transmission are 2.78 degrees and at 95%. The result for the inverse design using CNN returns the efficient inverse design of test data that can be designed according to the required pressure distribution. Wavelength 1000 nanometer to 1600 machine learning method absorbance 40% and ffDNN absorbance 33%. Show more

Keywords: Convolutional neural network, deep learning, machine learning, MRI lungs images, nanophotonics components mapping, pattern recognition

DOI: 10.3233/IDA-237436

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 95-114, 2023

Authors: Ali, Saif Mohammed | Burhanuddin, M.A. | Yaseen, Ali Taha | Jaber, Mustafa Musa | Jassim, Mustafa Mohammed | Ali, Aseel Mohammed | Alkhayyat, Ahmed | Mohammed, Mohammed A. | Mohamad, Auday A.H.

Article Type: Research Article

Abstract: The health records management issues have detrimentally affected the Iraqi healthcare sector resultant from the inferior information technology integrity and the complicatedness of data. In order to resolve this problem, other methods of storage, management, and retrieval of health-related data can be offered by e-Health services. These aspects are important in tracking patients’ health conditions using multiple platforms at the service provider’s own convenience. However, there are numerous issues that hinder the extensive adoption of e-Health services by the health establishments in Iraq, such as issues on security and privacy, legalities connected to policies, and its implementation. The …significance of the current study is its identification of the crucial aspects that will lead to the success of impacting the technical staff towards their positive acceptance and behavior with regard to the employment of e-Health information system in Iraqi hospitals. A self-administered survey was carried out on 104 technical staff from various healthcare organizations in Iraq using a simple random sampling technique. A nonparametric second-generation multivariate analysis was conducted on the compiled ordinal data by the utilization of the PLS-SEM approach. The outcomes indicated the favorable impact of several factors on the doctor’s employment of e-Health in Iraqi hospitals, comprising Availability and Affordability of the hardware and software, ICT Support Service, Network Reliability, Privacy, and Security. The results are important in assisting the comprehension of e-Health systems in the management of health data, in addition to the provision of the pertinent recommendations for policymakers to provide guidance, issue advice, directives to the healthcare professionals toward the continuous consideration of using advance information and communications technology at work. Show more

Keywords: e-Health, e-Health readiness, e-Healthcare, Iraqi e-Health, e-readiness

DOI: 10.3233/IDA-237438

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 115-135, 2023

Authors: Qi, Na | Zhang, Xun

Article Type: Research Article

Abstract: BACKGROUND: The aging of the population is a historical stage that many countries must experience, and the current design and development of elderly health care products can no longer meet the increasing demands of the elderly. OBJECTIVE: The impact of ethical design of elderly health care products on socio-economic development is explored to provide a theoretical basis for the development direction of elderly health care products. METHODS: In this study, a questionnaire survey is conducted on 268 middle-aged people to record the subjects’ willingness to purchase elderly health care products and their reasons, …concerns, satisfaction, and future demands. RESULTS: Among the subjects, 181 people have purchased elderly health care products, accounting for 67.36%; the subjects are more concerned about the quality and safety of elderly health care products, accounting for 92.56% and 91.85% respectively, followed by operability (68.46%); the problems encountered by the elderly using elderly health care products are mainly operational problems, accounting for 65.37%; and high safety (86.13%) and good quality (79.55%) are the subjects’ main demands for future development of elderly health care products. 73.61% of the 30–40 year old subjects said that the intelligent aged care products were very good; 65.89% of the 41–50 year old subjects said that the intelligent aged care products were very good; 52.67% of the 51–60 subjects thought that intelligent elderly care products were very good; and 47.82% of the subjects whose age were over 60 expressed their willingness to try intelligent elderly care products. CONCLUSIONS: Good quality and high safety are the main demands for the future development of elderly health care products. The elderly health care products manufactured based on the people-oriented design ethics concept can greatly meet the aspirations of the elderly to pursue a happy later life, and promote the vigorous development of the elderly industrial economy. Show more

Keywords: Population aging, elderly health care products, questionnaire survey, design ethics, people-oriented

DOI: 10.3233/IDA-237439

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 137-150, 2023

Authors: Liu, Xinghui | Tan, Hongwen | Liu, Xiaoqiao | Wu, Qiang

Article Type: Research Article

Abstract: OBJECTIVE: To explore changes in the plasma atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) in patients with left-to-right shunt congenital heart disease (CHD) before and in the early stage after interventional occlusion and to evaluate the clinical significance. METHODS: Among 97 patients with left-to-right shunt CHD undergoing interventional occlusion, 34 cases had a VSD (ventricular septal defect), 35 cases had an ASD (atrial septal defect), and 28 cases had PDA (patent ductus arteriosus). Another 20 normal adults formed the control group. An ELISA was used to determine the plasma ANP and BNP levels before …and on the third day after the operation to evaluate their correlations with cardiac functions and the defect size. RESULTS: The plasma ANP and BNP levels of patients with left-to-right shunt CHD were increased compared with those of the normal control group (P < 0.01), and the plasma ANP and BNP levels were decreased on the third day after interventional occlusion compared with the preoperative levels (P < 0.05). The plasma ANP and BNP levels were correlated with the New York Heart Association (NYHA) grade, left ventricular ejection fraction and defect diameter (P < 0.05). CONCLUSION: Patients with left-to-right congenital heart disease exhibit activation of ANP and BNP, which can be alleviated in the early stage after intervention occlusion. Left-to-right shunt congenital heart disease is given priority over atrial septal defect (ASD), ventricular septal defect (VSD) and patent ductus arteriosus (PDA). Early traditional methods included repair or correction by open heart surgery under extracorporeal circulation (also known as cardiopulmonary bypass, CPB). However, interventional therapy has become a developing trend for the treatment of congenital heart disease since 1967, when Porstmann et al. [1]. reported the transcatheter closure of ASD for the first time. The application of the AMPLATZER occluder, which is a simple and feasible method, has improved the safety of the treatment and enabled the therapeutic effect to reach ideal levels. The natriuretic peptide (NP) family consists of the atrial natriuretic polypeptide (ANP), the brain natriuretic peptide, which is also known as the B type natriuretic peptide (BNP), the C type natriuretic peptide (CNP), the renal natriuretic peptide (RNP) and the D type natriuretic peptide (DNP). These family members are similar in structure, have strong natriuretic, diuretic and vasodilative effects and antagonize the activity of the renin-angiotensin-aldosterone system (RAAS) and the sympathetic nerve. Together, the natriuretic peptides sensitively and specifically reflect the ventricular function state. Although all types of congenital heart disease differ in anatomical structure, they all contain the common features of heart failure. This study detected changes in the serum ANP and BNP levels in patients with left-to-right shunt congenital heart disease before and on the third day after interventional occlusion to evaluate the early changes in left-to-right shunt congenital heart disease after interventional occlusion through neuroendocrine. Show more

Keywords: Left-to-right shunt congenital heart disease, atrial natriuretic peptide, brain natriuretic peptide, interventional occlusion

DOI: 10.3233/IDA-237440

Citation: Intelligent Data Analysis, vol. 27, no. S1, pp. 151-159, 2023