Okure U. Obot

An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems

The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP.

Clinical decision support system (DSS) in the diagnosis of malaria: A case comparison of two soft computing methodologies

The purpose of this study is to make the case for the utility of decision support systems (DSS) in the diagnosis of malaria and to conduct a case comparison of the effectiveness of the fuzzy and the AHP methodologies in the medical diagnosis of malaria, in order to provide a framework for determining the appropriate kernel in a fuzzy-AHP hybrid system. The combination of inadequate expertise and sometimes the vague symptomatology that characterizes malaria, exponentially increase the morbidity and mortality rates of malaria. The task of arriving at an accurate medical diagnosis may sometimes become very complex and unwieldy. The challenge therefore for physicians who have limited experience investigating, diagnosing, and managing such conditions is how to make sense of these confusing symptoms in order to facilitate accurate diagnosis in a timely manner. The study was designed on a working hypothesis that assumed a significant difference between these two systems in terms of effectiveness and accuracy in diagnosing malaria. Diagnostic data from 30 patients with confirmed diagnosis of malaria were evaluated independently using the AHP and the fuzzy methodologies. Results were later compared with the diagnostic conclusions of medical experts. The results of the study show that the fuzzy logic and the AHP system can successfully be employed in designing expert computer based diagnostic system to be used to assist non-expert physicians in the diagnosis of malaria. However, fuzzy logic proved to be slightly better than the AHP, but with non-significant statistical difference in performance.

Fuzzy rule-based framework for the management of tropical diseases

The application of the conventional symbolic rules found in knowledge base technology to the management of a disease suffers from its inability to evaluate the degree of severity of a symptom and by extension, the degree of the illness. Fuzzy logic technology provides a simple way to arrive at a definite conclusion from vague, ambiguous, imprecise and noisy data (as found in medical data) using linguistic variables that are not necessarily precise. In order to achieve this, a study of a knowledge base system for the management of diseases was undertaken. The root sum square of drawing inference was employed to infer the data from the rules developed. This resulted in the establishment of some degrees of influence on the diseases. Using malaria as a case study, a system that uses Visual Basic .Net development environment was developed and the results of the computations are presented in this research.

A framework for fuzzy diagnosis of hepatitis

A study of the orthodox practice of diagnosing hepatitis revealed that inexactness in the diagnostic results has led several patients into abusing therapies. This prompted a further study into how this could be resolved. In this regard, effort was made for medical doctors to specify some linguistic labels while taking history and performing medical examinations on the patients. The effort yielded few responses which necessitated a study of the application of fuzzy logic technology to medical diagnosis. The symptoms were fuzzified with some membership functions which aided in the extraction of fuzzy rule base. With data and rules, fuzzy inference using the maxmin method was applied on the knowledge base, the results obtained were defuzzified to obtain crisp outputs that represent the diagnostic values with linguistic labels. The novelty of the result is that the degree or extent to which a patient suffers from hepatitis is reported to the patient and based on such revelation therapy would be administered without an abuse.

Application of Neuro-Fuzzy Technology in Medical Diagnosis: Case Study of Heart Failure

A neuro-fuzzy expert system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some from three hospitals in Nigeria with the assistance of their medical personnel who collected patients’ data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis.

A neuro-fuzzy decision support model for therapy of heart failure

Most medical decision support systems focus on diagnosis with little emphasis on therapy to the effect that though an accurate diagnosis is undertaken patients still have problems of drug misuse as a result of inaccurate therapy. The purpose of this paper is to design an assistive model for therapy of heart failure using artificial neural networks (ANN). Artificial neural networks have been found to be a very veritable tool in learning from existing datasets and based on the results, can perform accurate prediction on the data it has not encountered before through generalisation. It was based on this that 134 datasets on heart failure were collected from three hospitals and trained in a feed forward back propagation learning neural networks. This was further refined through the fuzzy system and some decision support filters. Results obtained from the neuro-fuzzy system indicate that the model has the ability to refine and enhance the physician’s ability to prescribe an appropriate therapy based on the diagnosis. This study is one of the few attempts at utilising soft computing technology in the diagnosis and therapy of cardiovascular diseases. The authors had previously developed neuro-fuzzy models for diagnosis of heart failure.

Speech Quality Enhancement in Digital Forensic Voice Analysis

The influence of noise and reverberation in Digital Forensic voice evidence can conceal the identification, verification and processing of crime data. Computationally, the efficiency in processing speech signals largely depends on the integrity and authenticity of audio/voice recordings. Our interest is on improving integrity, vis-a-vis the intelligibility of speech signals. We achieved this in four folds. First, a speech quality enhancement technique that cleans and rebuilds defective speech data for quality Forensic analysis is proposed by exploring an optimal estimator for the magnitude spectrum, where the Discrete Fourier Transform (DFT) coefficients of clean speech are modelled by a Laplacian distribution and the noise DFT coefficients are modelled using a Gaussian distribution. Second, an automatic speech pre-processing algorithm for phoneme segmentation of raw speech data, capable of iteratively refining Hidden Markov Model (HMM) speech labels for improved intelligibility is introduced. Third, a simulation of the distortion from a quantised R-bit and computation of the Signal-to-Noise Ratio (SNR) for the signal to quantisation noise is carried out for the purpose of managing speech signal distortions. Fourth, an investigation of the effect of confused phonemic and tone bearing unit features on the intelligibility of speech is presented to assist Forensic experts decode voice disguise or language “barriers” that may impede proper Forensic voice analysis. Results obtained in this investigation reveal a future of prospects in the field of Forensic intelligence and is most likely to reduce unnecessary setbacks during Forensic analysis.

Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease

Abstract In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.