A Novel Expert System for the Diagnosis and Treatment of Heart Disease

The diagnosis of diseases in their early stages can assist us in preventing life-threatening infections and caring for them better than in the last phase because prevention is better than cure. The death rate can be very high due to the unapproachability of diagnosed patients at an early point. Expert systems help us to defeat the problem mentioned above and enable us to automatically diagnose diseases in their early phases. Expert systems use a fuzzy, rule-based inference engine to provide forward-chain methods for diagnosing the patient. In this research, data have been gathered from different sources, such as a hospital, by performing the test on the patients’ age, gender, blood sugar, heart rate, and ECG to calculate the values. The proposed expert system for medical diagnosis can be used to find minimum disease levels and demonstrate the predominant method for curing different medical diseases, such as heart diseases. In the next step, the diagnostic test at the hospital with the novel expert system, the crisp, fuzzy value is generated for input into the expert system. After taking the crisp input, the expert system starts working on fuzzification and compares it with the knowledge base processed by the inference engine. After the fuzzification, the next step starts with the expert system in the defuzzification process converting the fuzzy sets’ value into a crisp value that is efficient for human readability. Later, the expert physician system’s diagnosis calculates the value by using fuzzy sets, and gives an output to determine the patient’s heart disease. In one case, the diagnosis step was accomplished, and the expert system provided the yield with the heart disease risk level as “low”, “high”, or “risky”. After the expert system’s responsibilities have been completed, the physician decides on the treatment and recommends a proper dose of medicine according to the level the expert system provided after the diagnosis step. The findings indicate that this research achieves better performance in finding appropriate heart disease risk levels, while also fulfilling heart disease patient treatment due to the physicians shortfalls

Machine Vision Approach for Diagnosing Tuberculosis (TB) Based on Computerized Tomography (CT) Scan Images

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts for early detection. This study represents a precise and reliable machine vision-based approach for Tuberculosis detection in the lung through Symmetry CT scan images. TB spreads irregularly, which means it might not affect both lungs equally, and it might affect only some part of the lung. That’s why regions of interest (ROI’s) from TB infected and normal CT scan images of lungs were selected after pre-processing i.e., selection/cropping, grayscale image conversion, and filtration, Statistical texture features were extracted, and 30 optimized features using F (Fisher) + PA (probability of error + average correlation) + MI (mutual information) were selected for final optimization and only 6 most optimized features were selected. Several supervised learning classifiers were used to classify between normal and infected TB images. Artificial Neural Network (ANN: n class) based classifier Multi-Layer Perceptron (MLP) showed comparatively better and probably best accuracy of 99% with execution time of less than a second, followed by Random Forest 98.83%, J48 98.67%, Log it Boost 98%, AdaBoostM1 97.16% and Bayes Net 96.83%