The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B

Although hepatitis B is widespread, it is hard to cure. This paper presents a new and more accurate model for the diagnosis and treatment of hepatitis B. Based on previous research, the diagnosis and treatment modes were combined into one. By adding more influencing factors and risk factors, the overall diagnosis and treatment model will be further expanded, and a richer and more detailed overall diagnosis and treatment model will be constructed. Reverse logic gates are used in the model to improve the accuracy of the treatment planning. The new unified model is more accurate in subdividing diagnosis results, and it is more flexible and accurate in providing dynamic treatment plans. The prediction process and the static diagnosis process of the model are symmetric, and the related sub-graph is symmetric in structure. In addition, an algorithm for predicting the response probability of treatment scheme is developed, so as to predict the subsequent treatment effects of the current treatment scheme, such as the probability of drug resistance. The results show that this method is more accurate than other available systems, and it has encouraging diagnostic accuracy and effectiveness, which provides a promising help for doctors in diagnosing hepatitis B.

Differential Diagnostic Reasoning Method for Benign Paroxysmal Positional Vertigo Based on Dynamic Uncertain Causality Graph

The accurate differentiation of the subtypes of benign paroxysmal positional vertigo (BPPV) can significantly improve the efficacy of repositioning maneuver in its treatment and thus reduce unnecessary clinical tests and inappropriate medications. In this study, attempts have been made towards developing approaches of causality modeling and diagnostic reasoning about the uncertainties that can arise from medical information. A dynamic uncertain causality graph-based differential diagnosis model for BPPV including 354 variables and 885 causality arcs is constructed. New algorithms are also proposed for differential diagnosis through logical and probabilistic inference, with an emphasis on solving the problems of intricate and confounding disease factors, incomplete clinical observations, and insufficient sample data. This study further uses vertigo cases to test the performance of the proposed method in clinical practice. The results point to high accuracy, a satisfactory discriminatory ability for BPPV, and favorable robustness regarding incomplete medical information. The underlying pathological mechanisms and causality semantics are verified using compact graphical representation and reasoning process, which enhance the interpretability of the diagnosis conclusions.

A Robust Decision Support System for Wireless Healthcare Based on Hybrid Prediction Algorithm

Analysis of healthcare data becomes a tedious task as large volume of unlabelled information is generated. In this article, an algorithm is proposed to reduce the complexity involved in analysis of healthcare data. The proposed algorithm predicts the health status of elderly from the data collected at health centres by utilizing PCA (principle component analysis) and SVM (support vector machine) algorithms. The performance of proposed algorithm is assessed by comparing it with well-known methods like quadratic Discriminant, linear Discriminant, logistic regression, KNN weighted and SVM medium Gaussian using F-measure. At that point, the pre-prepared information is subjected to the dimensionality decrease process by playing out the Feature Selection errand. So, chosen component analysis are investigated by the proposed work SVM-based enhanced recursive element determination, and its precision is assessed and contrasted with the other customary classifiers, for example, quadratic Discriminant, Linear Discriminant, Logistic Regression, KNN Weighted and SVM Medium Gaussian. Here, we built up a shrewd versatile information module for the remote procurement and transmission of EHR (Electronic Health Record) chronicles, together with an online watcher for showing the EHR datasets on a PC, advanced cell or tablet. So as to characterize the highlights required by clients, we demonstrated the elderly checking system in home and healing facility settings. Utilizing this data, we built up a portable information exchange module in light of a Raspberry Pi.

A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications

BACKGROUND AND OBJECTIVE

A high percentage of medical errors, committed because of physician's lack of experience, huge volume of data to be analyzed, and inaccessibility to medical records of previous patients, can be reduced using computer-aided techniques. Therefore, designing more efficient medical decision-support systems (MDSSs) to assist physicians in decision-making is crucially important. Through combining the properties of fuzzy logic and neural networks, fuzzy cognitive maps (FCMs) are among the latest, most efficient, and strongest artificial intelligence techniques for modeling complex systems. This review study is conducted to identify different FCM structures used in MDSS designs. The best structure for each medical application can be introduced by studying the properties of FCM structures.

METHODS

This paper surveys the most important decision- making methods and applications of FCMs in the medical field in recent years. To investigate the efficiency and capability of different FCM models in designing MDSSs, medical applications are categorized into four key areas: decision-making, diagnosis, prediction, and classification. Also, various diagnosis and decision support problems addressed by FCMs in recent years are reviewed with the goal of introducing different types of FCMs and determining their contribution to the improvements made in the fields of medical diagnosis and treatment.

RESULTS

In this survey, a general trend for future studies in this field is provided by analyzing various FCM structures used for medical purposes, and the results from each category.

CONCLUSIONS

Due to the unique specifications of FCMs in integrating human knowledge and experience with computer-aided techniques, they are among practical instruments for MDSS design. In the not too distant future, they will have a significant role in medical sciences.

Intelligence System for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm

OBJECTIVES

Coronary heart disease is the leading cause of death worldwide, and it is important to diagnose the level of the disease. Intelligence systems for diagnosis proved can be used to support diagnosis of the disease. Unfortunately, most of the data available between the level/type of coronary heart disease is unbalanced. As a result system performance is low.

METHODS

This paper proposes an intelligence systems for the diagnosis of the level of coronary heart disease taking into account the problem of data imbalance. The first stage of this research was preprocessing, which included resampled non-stratified random sampling (R), the synthetic minority over-sampling technique (SMOTE), clean data out of range attribute (COR), and remove duplicate (RD). The second step was the sharing of data for training and testing using a k-fold cross-validation model and training multiclass classification by the K-star algorithm. The third step was performance evaluation. The proposed system was evaluated using the performance parameters of sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), area under the curve (AUC) and F-measure.

RESULTS

The results showed that the proposed system provides an average performance with sensitivity of 80.1%, specificity of 95%, PPV of 80.1%, NPV of 95%, AUC of 87.5%, and F-measure of 80.1%. Performance of the system without consideration of data imbalance provide showed sensitivity of 53.1%, specificity of 88,3%, PPV of 53.1%, NPV of 88.3%, AUC of 70.7%, and F-measure of 53.1%.

CONCLUSIONS

Based on these results it can be concluded that the proposed system is able to deliver good performance in the category of classification.