Mohd Khanapi Abd Ghani

Electronic health records approaches and challenges: a comparison between Malaysia and four East Asian countries

An integrated Lifetime Health Record (LHR) is fundamental for achieving seamless and continuous access to patient medical information and for the continuum of care. However, the aim has not yet been fully realised. The efforts are actively progressing around the globe. Every stage of the development of the LHR initiatives had presented peculiar challenges. The best lessons in life are those of someone elses experiences. This paper presents an overview of the development approaches undertaken by four East Asian countries in implementing a national Electronic Health Record (EHR) in the public health system. The major challenges elicited from the review including integration efforts, process reengineering, funding, people, and law and regulation will be presented, compared, discussed and used as lessons learned for the further development of the Malaysian integrated LHR.

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

Abstract Context The Vehicle Routing Problem (VRP) has numerous applications in real life. It clarifies in a wide area of transportation and distribution such as transportation of individuals and items, conveyance service and garbage collection. Thus, an appropriate selecting of vehicle routing has an extensive influence role to improve the economic interests and appropriateness of logistics planning. Problem In this study the problem is as follows: Universiti Tenaga Nasional (UNITEN) has eight buses which are used for transporting students within the campus. Each bus starts from a main location at different times every day. The bus picks up students from eight locations inside the campus in two different routes and returns back to the main location at specific times every day, starting from early morning until the end of official working hours, on the following conditions: Every location will be visited once in each route and the capacity of each bus is enough for all students included in each route. Objectives Our paper attempt to find an optimal route result for VRP of UNITEN by using genetic algorithm. To achieve an optimal solution for VRP of UNITEN with the accompanying targets: To reduce the time consuming and distance for all paths. which leads to the speedy transportation of students to their locations, to reduce the transportation costs such as fuel utilization and additionally the vehicle upkeep costs, to implement the Capacitated Vehicle Routing Problem (CVRP) model for optimizing UNITEN’s shuttle bus services. To implement the algorithm which can be used and applied for any problems in the like of UNITEN VRP. Approach The Approach has been presented based on two phases: firstly, find the shortest route for VRP to help UNITEN University reduce student’s transportation costs by genetic algorithm is used to solve this problem as it is capable of solving many complex problems; secondly, identify The CVRP model is implemented for optimizing UNITEN shuttle bus services. Finding The findings outcome from this study have shown that: (1) A comprehensive listed of active GACVRP; (2) Identified and established an evaluation criterion for GACVRP of UNITEN; (3) Highlight the methods, based on hybrid crossover operation, for selecting the best way (4) genetic algorithm finds a shorter distance for route A and route B. The proportion of reduction the distance for each route is relatively short, but the savings in the distance becomes greater when calculating the total distances traveled by all buses daily or monthly. This applies also to the time factor that has been reduced slightly based on the rate of reduction in the distances of the routes.

Critical analysis of the usage of patient demographic and clinical records during doctor-patient consultations: a Malaysian perspective

The Lifetime Health Record (LHR) is the central key delivery component of Malaysias integrated telehealth application. The LHR consists of the summarised health records of every individual compiled from their Electronic Medical Record (EMR). The most important consideration however is that the patient health record should not only be available and accessible seamlessly, but also has the ability to be continuously captured by healthcare professionals. This research aims to analyse and identify the most crucial patient demographic and clinical records that are referred to and recorded by the doctors during the consultation encounter.

Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma

Abstract Nasopharyngeal Carcinoma (NPC) diagnostic is a challenging issue that have not been optimally solved. NPC has a complex structure which makes it difficult to diagnose even by an expert physician. Many researchers over the last few decades till now have established a lot of research efforts and used many methods with different techniques. However, the best solution to resolve the mentioned issue is very complex and needs innovative methods to find the optimal solutions. The study presents a novel automatic segmentation and identification for NPC by artificial neural networks from microscopy images without human intervention by developing the best characteristics towards preliminary NPC cases discovery. For getting accurate region of NPC in the microscope image, we propose a novel NPC segmentation method that has three major innovation points. First, K -means clustering will be used in the first stage after enhancing the image to be labelled in the regions based on their colour. Second, neural network has been employed to select the right object based on training stage. Third, texture feature for the segmented region will extract to ted to the segmentation. Regarding to the identification, the colour features have used to diagnose the ovarian tumours to the differential between benign and malignant. The findings outcome from this study have shown that: (1) A new adaptive method has been used as post-processing in detecting NPC, (2) Identified and established an evaluation criterion for automatic segmentation and identification of NPC cases, (3) Highlight the methods: based on region growing based technique and K -means clustering method for selecting the best region and (4) Assessed the efficiency of the anticipated results by associating ANN and SVM segmentation results, and automatic NPC classification. Also indicate that the texture features have some extra value or added value in separating benign from malignant. Therefore, we can use the proposed system, first, as indicator to diagnosis the case, second, use it as a support tool for the doctor to support his decision. We evaluated the effectiveness of the framework by firstly comparing the automatic segmentation against the manual, and then integrating the proposed segmentation solution into a classification framework for identifying benign and malignant tumour. Both test results show that the method is effective in segmentation the region of interest which is around 88.03% Consequently, this rate expanded to 91.01% when line presumption (NPC classification) based on ANN technique is employed with high level accuracy of classification (sensitivity) of 93.42% and specificity of 90.01%.

Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach

Abstract Context Nasopharyngeal carcinoma (NPC) is a type of cancer in the head and neck, and this cancer presents in the throat region between the pharynx and nasal cavity. NPC is frequently detected in Southeast Asia, particularly in the southern part of China, Malaysia, Singapore, Hong Kong, Taiwan, Vietnam, and Thailand. Problem The diagnostic procedure of NPC entirely depends on the Physicians experience and involves multiple subjective decisions. Subjective decision-making can result in inter and intra observer variations. Inter-observer variation is the total difference obtained from the results of two and above observers when scrutinizing similar materials. Variation amongst the observers is the total difference an observer experience when spotting the same material many times. Tradition diagnostic of NPC has many limitations such as the time consuming for doctors to identify and recognize the tumor area slice by slice and reduce radiologists’ workloads. In addition, another challenge lies in the appearance of doctors used the observation of human eyes (human errors) in NPC cases can be missed detailed information. Approach A novel approach to automatic segmentation plus initial seed generated without human intervention of nasopharyngeal carcinoma using region growing based technique from microscopy images is presented in this study by take advantage of geometric features to detection of NPC images. In order to get accurate region of NPC image, the proposed results utilize wavelet transform for image enhancement by reduce the noise by remove the high ratio sub-bands and predestine a developed NPC image. Segmentation steps including many phases. Firstly, the thresholding is mean value used to binarise the image and secondly, filtering or remove unwanted objects in the images. Finding The findings outcome from this study have shown that: (1) a new adaptive threshold is used as a post-processing to at long last detect the NPC; (2) identified and established an evaluation criterion for automatic segmentation of NPC cases; (3) highlight the methods, based on region growing based technique and active contour operation, for selecting the best region; (4) assessed the performance of the proposed results by comparing the manual measurements and automatic NPC segmentation. The NPC segmentation rate in the technique used is about 83.89%. Comparably, this amount expanded to 92.04% once a line presumption (NPC approximation) was utilized in one of the stage in the technique here.

A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear

Abstract Nasopharyngeal carcinoma (NPC) is a serious disease with diverse prognoses and the diffusive development of the tumors further complicates the diagnosis. However, in most cases, surgery is performed by resecting the tumor that decides the life expectancy of a patient. Certainly, the graphical portrayal is a fundamental factor to distinguish and examine an NPC tumor; and, the exact nasopharyngeal carcinoma perception remains an important errand. It is crucial to improve the extent of resection for the irregular tissues while sparing the normal ones. There are several methods to envision the nasopharyngeal carcinoma, but the main problem with these strategies is the inability to imagine the border points of the nasopharyngeal tumor accurately in detail. In addition, the inability to separate the normal tissues from the undesirable ones prompts the assessment and calculation of a wrong tumor measure. NPC diagnosis is a difficult and challenging process owing to the possible shapes and regions of tumors and intensity of the images. The pathological identification of the nasopharyngeal carcinoma and comparing typical and anomalous tissues require a set of scientific strategies for the extraction of features. The aim of this paper was to outline and assess a novel method using machine learning approaches based on genetic algorithm for NPC feature selection and artificial neural networks for an automated NPC detection of the NPC tissues from endoscopic images. The proposed approach was validated by comparing the number of NPC identified through this technique against the manual checking by the ENT specialists. The classifier lists a high precision of 96.22%, the sensitivity of 95.35%, and specificity of 94.55%. Additionally, the feature chosen process makes the Artificial Neural Networks classifier straightforward and more efficient.

Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution

Abstract Context Vehicle routing problem (VRP) is one of the many difficult issues that have no perfect solutions yet. Many researchers over the last few decades have established numerous researches and used many methods with different techniques to handle it. But, for all research, finding the lowest cost is very complex. However, they have managed to come up with approximate solutions that differ in efficiencies depending on the search space. Problem In this study the problem is as follows: have a number of vehicles which are used for transporting applications to instance place. Each vehicle starts from a main location at different times every day. The vehicle picks up applications from start locations to the instance place in many different routes and return back to the start location in at specific times every day, starting from early morning until the end of official working hours, on the following conditions: (1) Every location will be visited once in each route, and (2) The capacity of each vehicle is enough for all applications included in each route. Objectives Our paper attempt to find an optimal route result for VRP by using K-Nearest Neighbor Algorithm (KNNA). To achieve an optimal solution for VRP with the accompanying targets: (1) To reduce the distance and the time for all paths this leads to speedy the transportation of customers to their locations, (2) To implement the capacitated vehicle routing problem (CVRP) model for optimizing the solutions. Approach The approach has been presented based on two phases: firstly, the algorithms have been adapted to solve the research problem, where its procedure is different than the common algorithm. The structure of the algorithm is designed so that the program does not require a large database to store the population, which speeds up the implementation of the program execution to obtain the solution; secondly, the algorithm has proven its success in solving the problem and finds a shortest route. For the purpose of testing the algorithm’s capability and reliability, it was applied to solve the same problem online validated and it achieved success in finding a shorter route. Finding The findings outcome from this study have shown that: (1) A universal listed of dynamic KNNACVRP; (2) Identified and built up an assessment measure for KNNACVRP; (3) Highlight the strategies, based KNNA operations, for choosing the most ideal way (4) KNNA finds a shorter route for VRP paths. The extent of lessening the distance for each route is generally short, but the savings in the distance becomes more noteworthy while figuring the aggregate distances traveled by all transports day by day or month to month. This applies likewise to the time calculate that has been decreased marginally in view of the rate of reduction in the distances of the paths.

Review on Nasopharyngeal Carcinoma: Concepts, methods of analysis, segmentation, classification, prediction and impact: A review of the research literature

Abstract Context Nasopharyngeal Carcinoma (NPC) is the most famous type of tumor in the neck and started in the nasopharynx, the area at the top of the pharynx or “throat”, in which the participation of the relevant nose and tube sound including all upper respiratory tract. Purpose The study is a reviewed literature on NPC Diagnosis. The objectives of the paper are to understand; (1) The conceptual definitions of Nasopharyngeal Carcinoma, (2) the descriptive nature of the structure of the prediction NPC, (3) The contextual usage of NPC, (4) who have access to the NPC, (5) the nature and components of the data used in NPC study, (6) what are the objectives of this field of research (7) what data collection techniques were employed in the previous researches (8) what were the outcome of the previous studies. Methodology Until this time no systematic literature reviews (SLR) were conducted on NPC based on Segmentation, Classification, and Prediction. The aim of the study therefore, is to conduct a systematic review, classification and comparison of the previous methodology and approaches used in the studies on Nasopharyngeal Carcinoma based on Segmentation, Classification, and Prediction composition (published between 1970 and 2016). We therefore systematically reviewed available researches related to the NPC. We search for available literature using five electronics databases: ScienceDirect.com | Science, health and medical journals, IEEE – The worlds largest technical & professional organization, Springer – International Publisher Science, Technology, Medicine, ACM digital libraries and Google Scholar. Results The concept of NPC encompasses vast information technologies, there are few papers that dwelt on explanations on the NPC structure or the terminology used, synopsis on the various methods and technologies involved in NPC, the contents of NPC, The various findings on NPC developmental work, Data Analysis, Segmentation, Classification, Prediction and Impacts of Nasopharyngeal Carcinoma. In this study, offered an explanation for NPC defined, how structure prediction Nasopharyngeal Carcinoma described, in what context NPC used, who has access to the NPC, the component data of the NPC used and studied, what is the purpose of research in this field, what methods of data collection was used in the review and whether the results of this study. Also an impression of all the several of methods and technologies involved in NPC, A synopsis of the details of NPC and the result that in NPC development work, segmentation of NPC, NPC diagnoses or the prognosis process.

Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network

The segregation among benign and malignant nasopharyngeal carcinoma (NPC) from endoscopic images is one of the most challenging issues in cancer diagnosis because of the many conceivable shapes, regions, and image intensities, hence, a proper scientific technique is required to extract the features of cancerous NPC tumors. In the present research, a neural network-based automated discrimination system was implemented for the identification of malignant NPC tumors. In the proposed technique, five different types of qualities, such as local binary pattern, gray-level co-occurrence matrix, histogram of oriented gradients, fractal dimension, and entropy, were first determined from the endoscopic images of NPC tumors and then the following steps were executed: (1) an enhanced adaptive approach was employed as the post-processing method for the classification of NPC tumors, (2) an assessment foundation was created for the automated identification of malignant NPC tumors, (3) the benign and cancerous cases were discriminated by using region growing method and artificial neural network (ANN) approach, and (4) the efficiency of the outcomes was evaluated by comparing the results of ANN. In addition, it was found that texture features had significant effects on isolating benign tumors from malignant cases. It can be concluded that in our proposed method texture features acted as a pointer as well as a help instrument to diagnose the malignant NPC tumors. In order to examine the accuracy of our proposed approach, 159 abnormal and 222 normal cases endoscopic images were acquired from 249 patients, and the classifier yielded 95.66% precision, 95.43% sensitivity, and 95.78% specificity.

Enabling technologies for fog computing in healthcare IoT systems

Abstract Context: A fog computing architecture that is geographically distributed and to which a variety of heterogeneous devices are ubiquitously connected at the end of a network in order to provide collaboratively variable and flexible communication, computation, and storage services. Fog computing has many advantages and it is suited for the applications whereby real-time, high response time, and low latency are of the utmost importance, especially healthcare applications. Objectives: The aim of this study was to present a systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyze the previous. Providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field. Methods: The investigations were systematically performed on fog computing in the healthcare field by all studies; furthermore, the four databases Web of Science (WoS), ScienceDirect, IEEE Xplore Digital Library, and Scopus from 2007 to 2017 were used to analyze their architecture, applications, and performance evaluation. Results: A total of 99 articles were selected on fog computing in healthcare applications with deferent methods and techniques depending on our inclusion and exclusion criteria. The taxonomy results were divided into three major classes; frameworks and models, systems (implemented or architecture), review and survey. Discussion: Fog computing is considered suitable for the applications that require real-time, low latency, and high response time, especially in healthcare applications. All these studies demonstrate that resource sharing provides low latency, better scalability, distributed processing, better security, fault tolerance, and privacy in order to present better fog infrastructure. Learned lessons: numerous lessons related to fog computing. Fog computing without a doubt decreased latency in contrast to cloud computing. Researchers show that simulation and experimental proportions ensure substantial reductions of latency is provided. Which it is very important for healthcare IoT systems due to real-time requirements. Conclusion: Research domains on fog computing in healthcare applications differ, yet they are equally important for the most parts. We conclude that this review will help accentuating research capabilities and consequently expanding and making extra research domains.