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, Malaria
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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.