HIV analysis using computational intelligence

In this study, a new method to analyze HIV using a combination of autoencoder networks and genetic algorithms is proposed. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey. The autoencoder model is then compared with a conventional feedforward neural network model and yields a classification accuracy of 92% compared to 84% obtained for the conventional feedforward model. The autoencoder model is then used to propose a new method of approximating missing entries in the HIV database using ant colony optimization. This method is able to estimate missing input to an accuracy of 80%. The estimated missing input values are then used to analyze HIV. The autoencoder network classifier model yields a classification accuracy of 81% in the presence of missing input values. The feedforward neural network classifier model yields a classification accuracy of 82% in the presence of missing input values. A control mechanism is proposed to assess the effect of demographic properties on the HIV status of individuals, based on inverse neural networks, and autoencoder networks-based-on-genetic algorithms. This control mechanism is aimed at understanding whether HIV susceptibility can be controlled by modifying some of the demographic properties. The inverse neural network control model has accuracies of 77% and 82%, meanwhile the genetic algorithm model has accuracies of 77% and 92%, for the prediction of educational level of individuals, and gravidity, respectively. HIV modelling using neuro-fuzzy models is then investigated, and rules are extracted, which provide more valuable insight. The classification accuracy obtained by the neuro-fuzzy model is 86%. A rough set approximation is then investigated for rule extraction, and it is found that the rules present simplistic and understandable relationships on how the demographic properties affect HIV risk. The study concludes by investigating a model for automatic relevance determination, to determine which of the demographic properties is important for HIV modelling. A comparison is done between using the full input data set and the data set using the input parameters selected by the technique for the HIV classification. Age of the individual, gravidity, province, region, reported pregnancy and educational level were amongst the input parameters selected as relevant for classification of an individual’s HIV risk. This study thus proposes models, which can be used to understand HIV dynamics, and can be used by policy-makers to more effectively understand the demographic influences driving HIV infection

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Computational intelligence techniques for missing data imputation

Despite considerable advances in missing data imputation techniques over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions, including the Expectation Maximisation (EM), and the combination of autoassociative neural networks and genetic algorithms (NN-GA). The merits of both these techniques have been discussed at length in the literature, but have never been compared to each other. This thesis contributes to knowledge by firstly, conducting a comparative study of these two techniques.. The significance of the difference in performance of the methods is presented. Secondly, predictive analysis methods suitable for the missing data problem are presented. The predictive analysis in this problem is aimed at determining if data in question are predictable and hence, to help in choosing the estimation techniques accordingly. Thirdly, a novel treatment of missing data for online condition monitoring problems is presented. An ensemble of three autoencoders together with hybrid Genetic Algorithms (GA) and fast simulated annealing was used to approximate missing data. Several significant insights were deduced from the simulation results. It was deduced that for the problem of missing data using computational intelligence approaches, the choice of optimisation methods plays a significant role in prediction. Although, it was observed that hybrid GA and Fast Simulated Annealing (FSA) can converge to the same search space and to almost the same values they differ significantly in duration. This unique contribution has demonstrated that a particular interest has to be paid to the choice of optimisation techniques and their decision boundaries. iii Another unique contribution of this work was not only to demonstrate that a dynamic programming is applicable in the problem of missing data, but to also show that it is efficient in addressing the problem of missing data. An NN-GA model was built to impute missing data, using the principle of dynamic programing. This approach makes it possible to modularise the problem of missing data, for maximum efficiency. With the advancements in parallel computing, various modules of the problem could be solved by different processors, working together in parallel. Furthermore, a method for imputing missing data in non-stationary time series data that learns incrementally even when there is a concept drift is proposed. This method works by measuring the heteroskedasticity to detect concept drift and explores an online learning technique. New direction for research, where missing data can be estimated for nonstationary applications are opened by the introduction of this novel method. Thus, this thesis has uniquely opened the doors of research to this area. Many other methods need to be developed so that they can be compared to the unique existing approach proposed in this thesis. Another novel technique for dealing with missing data for on-line condition monitoring problem was also presented and studied. The problem of classifying in the presence of missing data was addressed, where no attempts are made to recover the missing values. The problem domain was then extended to regression. The proposed technique performs better than the NN-GA approach, both in accuracy and time efficiency during testing. The advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time. Lastly, instead of using complicated techniques to estimate missing values, an imputation approach based on rough sets is explored. Empirical results obtained using both real and synthetic data are given and they provide a valuable and promising insight to the problem of missing data. The work, has significantly confirmed that rough sets can be reliable for missing data estimation in larger and real databases

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income