A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES

Liver cirrhosis is the most common type of chronic liver disease in the globe. The ability to forecast the onset of liver cirrhosis sickness is critical for successful treatment and the prevention of catastrophic health implications. As a result, the researchers created a prediction model using machine learning techniques. This study was based on a dataset from the Federal Medical Centre, Yola, which included 583 patient instances and 11 attributes. The proposed model for the prediction of liver cirrhosis sickness employed Nave Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, and F1 Score were used to evaluate the model's performance. Among all the strategies used in this study, the Support Vector Machine (SVM) technique produces the best results, with accuracy of 73%, precision of 73%, recall of 100%, and F1 Score of 84%. Based on medical data from FMC, Yola, this study shows that machine learning methods, specifically the Support Vector Machine, provide a more accurate prediction for liver cirrhosis sickness. This approach can be used to help doctors make better clinical decisions

A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE

The rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%. Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining

Antimicrobial resistance pattern of methicillin-resistant Staphylococcus aureus isolated from sheep and humans in Veterinary Hospital Maiduguri, Nigeria

BACKGROUND AND AIM : Methicillin-resistant Staphylococcus aureus (MRSA), an important opportunistic pathogen, is a Gram-positive coccus known to be resistant to β-lactam antibiotics. Its virulence depends on a large range of factors, mainly extracellular proteins, such as enzymes and exotoxins, that contribute to causing a wide range of diseases in human and animal species. The major reasons for the success of this pathogen are its great variability, which enables it to occur and thrive at different periods and places with diverse clonal types and antibiotic resistance patterns within regions and countries. Infections caused by antibiotic-resistant S. aureus bring about serious problems in the general population (humans and animals). Infections with these pathogens can be devastating, particularly for the very young, adults and immunocompromised patients in both humans and animals. This study aimed to determine the presence of MRSA in both apparently healthy and sick sheep brought to the veterinary hospital as well as veterinary staff and students on clinical attachment in the hospital. MATERIALS AND METHODS : A total of 200 nasal swab samples were collected aseptically from sheep and humans (100 each) for the isolation of MRSA. The samples were processed by appropriately transporting them to the laboratory, then propagated in nutrient broth at 37°C for 24 h followed by subculturing on mannitol salt agar at 37°C for 24 h, to identify S. aureus. This was followed by biochemical tests (catalase and coagulase tests) and Gram staining. MRSA was isolated using Clinical Laboratory Standard Institute (CLSI) guideline and confirmed by plating onto Oxacillin (OX) Resistance Screening Agar Base agar. The antimicrobial susceptibility pattern of the MRSA isolates was determined using the disk diffusion method against 12 commonly used antimicrobial agents. RESULTS : The total rate of nasal carriage of S. aureus and MRSA was found to be 51% and 43% in sheep and humans, respectively. The MRSA prevalence in male and female sheep was 18% and 8%, while 9% and 8% were for male and female human samples, respectively. The antimicrobial susceptibility test showed 100% resistance to OX, cefoxitin, oxytetracycline, cephazolin, and penicillin-G (Pen) by MRSA isolates from humans. Conversely, there was 100% susceptibility to ciprofloxacin, imipenem, and gentamicin; for linezolid (LZD), it was 87.5%, norfloxacin (NOR) (71%), and erythromycin (ERY) (50%) susceptibility was recorded. The MRSA isolates from sheep recorded 100% resistance to the same set of drugs used for human MRSA isolates and were equally 100% susceptible to gentamicin, imipenem, LZD, ciprofloxacin, NOR (92%), and ERY (50%). CONCLUSION : This study determined the presence of MRSA in sheep and humans from the Veterinary Hospital, Maiduguri. It appears that certain drugs such as ciprofloxacin, imipenem, and gentamicin will continue to remain effective against MRSA associated with humans and sheep. Reasons for the observed patterns of resistance must be explored to reduce the burdens of MRSA resistance. Furthermore, the present study did not confirm the MRSA resistance genes such as mecA and spa typing to ascertain the polymorphism in the X-region using appropriate molecular techniques. Hence more studies need to be conducted to elucidate these findings using robust techniques.http://www.veterinaryworld.orgam2023Production Animal StudiesVeterinary Tropical Disease

EXPERT SYSTEM FOR DIAGNOSIS OF MALARIA AND TYPHOID

An expert system is a computer program designed to solve problems in a domain that has human expertise. The knowledge built into the system is usually obtained from experts in the field. Based on this knowledge, an expert system can replicate the thinking process of the human experts and make logical deductions accordingly. Malaria and Typhoid are major health challenge in our society today (Nigeria), its symptoms can lead to other illness which include prolonged fever, fatigue, headaches, nausea, abdominal pain and constipation or diarrhea. People in endemic areas are at risk of contracting both infections concurrently. According to the world malaria report 2011, there were about 216 million cases of malaria and typhoid and estimated 655,000 deaths in 2010. (WHO report, 2011). The main challenging issue confronting the healthcare is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly. This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable. Although many studies were carried out by different researchers in the medical domain using various data techniques. In this research work, an efficient expert system that diagnoses patients with malaria and typhoid was developed. A secondary data was collected from university of Maiduguri teaching hospital for the period of four years which ranges from 2017 to 2020. The work explored the potential benefits of proposing a new model for prediction and diagnosis of malaria and typhoid using symptoms. The model adopted the Naive bayes and was implemented using the python. The system diagnoses a patient in real time (within 30 minutes) without necessarily visiting the laboratory for a test. Three algorithms were used these are, Support vector machine, Artificial neural network and Naïve bayes. From our finding, it is observed that Naïve bayes and support vector machine give the best result which is 100% in terms of accuracy of diagnosis. Keywords: Diagnosis, Prediction, Expert System, Typhoid, Malari

THE PREDICTION OF HEPATITIS B VIRUS (HBV) USING ARTIFICIAL NEURAL NETWORK (ANN) AND GENETIC ALGORITHM (GA)

The hepatitis B virus causes a liver infection called hepatitis B (HBV). It might be severe and go away on its own. Some kinds, however, can be persistent, leading to cirrhosis and liver cancer. HBV can be transmitted to others without the individual being aware of it; some persons have no symptoms, while others only have the first infection, which later resolves. Others develop a chronic illness as a result of their condition. In chronic cases, the virus attacks the liver for an extended period of time without being detected, causing irreparable liver damage. The manual approach has a high number of errors due to human decision-making, and visual screening is time-consuming, tiresome, and costly in terms of manpower. To predict the occurrence of Hepatitis virus (HBV), this research project thesis suggested an algorithm; Artificial Neural Network (ANN), and genetic algorithm (GA). To develop, evaluate and validate the performance of the model developed using ANN. Medical records of nine hundred patients were collected in the Northern Senatorial District (Mubi South), Central Senatorial District (Hong), and Southern Senatorial District (Ganye) regions of Adamawa state, Nigeria. Three hundred (300) patient records were collected from each general hospital, for a total of 900 patient records. The success of the proposed technique is demonstrated when ANN is paired with GA, Accuracy (66.30%), Specificity (66.33%), and Sensitivity (77.53%) were discovered. In this study, hepatitis B virus (HBV) was predicted using Artificial Neural Network (ANN) classifier and Genetic algorithm optimization tool were used to select the features that are responsible for hepatitis B virus (Sex, Loss of Appetite, Nausea and vomiting, Yellowish skin and eye, Stomach pain, Pain in muscles and joint). The prediction was found to have acceptable performance measures which will reduce future incidence of the outbreak and aid timely response of medical experts. Keywords: Hepatitis B Virus (HBV), Prediction, Features, Classification

Wavelet Transform Technique Applied to Satellite Image Denoising

Satellite images either digital or analog must have certain elements that are accidentally introduced during the processing of capturing as a result of weather or system sensor known as electronic noise. However, several attempts and advances have been made by academicians, industries and intelligent security agencies to remove this noise. It has been a nagging problem in the area of computer vision, image processing and artificial intelligence to denoise satellite images and noise removal is among the significant components in satellite image analysis. The aim of this research work was to denoise the satellite image of Sambisa forest using the wavelet transform technique. Satellite images of Sambisa forest captured by Landsat satellite in 2007, 2013, 2014, 2019 and 2021 respectively with their associated Geo-referenced 11.2503° N Longitude and 13.4167° E Latitude were downloaded from the United States Geological Survey (USGS) website. The images are acquired as Zipped Geo-referenced Tagged Image File Format (GeoTIFF). Color Composite bands of natural colors (bands 2, 3 and 4) are combined using the ArcGIS software and RGB image were obtained. Wavelet transforms denoising technique was used to filter noise from the images, which was implemented using the wdenoise2() function in MATLAB 2021

Ensemble Model for Heart Disease Prediction

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. The heart is one of the essential parts of the human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical decision support systems to enhance the ability to diagnose and predict heart disease in humans. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researcher looks at how to use the ensemble model, which proposes a more stable performance than the use of a base learning algorithm and these lead to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher Bagging meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, according to the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has a high prediction probability score in the implementation of heart disease prediction