Adel Lahsasna

Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis

In the present paper, a fuzzy rule-based system (FRBS) is designed to serve as a decision support system for Coronary heart disease (CHD) diagnosis that not only considers the decision accuracy of the rules but also their transparency at the same time. To achieve the two above mentioned objectives, we apply a multi-objective genetic algorithm to optimize both the accuracy and transparency of the FRBS. In addition and to help assess the certainty and the importance of each rule by the physician, an extended format of fuzzy rules that incorporates the degree of decision certainty and importance or support of each rule at the consequent part of the rules is introduced. Furthermore, a new way for employing Ensemble Classifiers Strategy (ECS) method is proposed to enhance the classification ability of the FRBS. The results show that the generated rules are humanly understandable while their accuracy compared favorably with other benchmark classification methods. In addition, the produced FRBS is able to identify the uncertainty cases so that the physician can give a special consideration to deal with them and this will result in a better management of efforts and tasks. Furthermore, employing ECS has specifically improved the ability of FRBS to detect patients with CHD which is desirable feature for any CHD diagnosis system.

AMI Screening Using Linguistic Fuzzy Rules

This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.

An improved genetic-fuzzy system for classification and data analysis

Interpretability of classification systems, which refers to the ability of these systems to express their behavior in an understandable way, has recently gained more attention and it is considered as an important requirement especially for knowledge-based systems. The main objective of this study is to improve the ability of a well-known fuzzy classifier proposed in Ishibuchi and Nojima (2007) to maximize the accuracy while preserve its interpretability. To achieve the above-mentioned objective, we propose two variants of the original fuzzy classifier. In the first variant classifier, the same components of the original classifier were used except NSGA-II which was replaced by an enhanced version called Controlled Elitism NSGA-II. This replacement aims at improving the ability of the first variant classifier to find non-dominated solutions with better interpretability-accuracy trade-off. In the second variant classifier, we further improve the first variant classifier by enhancing the selection method of the antecedent conditions of the rules generated in the initial population of genetic algorithm. Unlike the method applied in the original classifier and the first variant classifier, which uses a random selection of the antecedent conditions, we proposed a feature-based selection method to favor the antecedent conditions associated with the most relevant features. The results show that the two variant classifiers find more non-dominated fuzzy rule-based systems with better generalization ability than the original method which suggests that Controlled Elitism NSGA-II algorithm is more efficient than NSGA-II. In addition, feature-based selection method applied in the second variant classifier allowed this method to successfully obtain high-quality solutions as it has consistently achieved the best error rates for all the data sets compared to the original method and the first variant classifier.

A Transparent Classification Model Using a Hybrid Soft Computing Method

Due to the inherent complexity of many real-world problems, classification models have become an important tool for solving pattern recognition tasks in many disciplines such as medicine, finance and management. Accuracy and transparency are two important criteria that should be satisfied by any classification model. In this paper, a transparent and relatively accurate classifier is developed using a hybrid soft computing technique. The initial fuzzy model is first generated using a clustering method and the transparency and accuracy of the model are then simultaneously optimized using a multi-objective evolutionary technique. The proposed model is tested on two real problems; the first one is related to credit scoring problem while the otheris on medical diagnosis. All the data sets used in this study are publicly available at UCI repository of machine learning database.

Credit risk evaluation decision modeling through optimized fuzzy classifier

Transparent decision support systems in the finance sector have an important role in the analysis and the decision process. This paper proposes a relatively transparent credit scoring model for evaluating the creditworthiness of the credit applicants through a hybrid data mining approach. The main motivation to apply this approach is to obtain a credit scoring model from a data set that not only have the required performance, but it is relatively interpretable. This objective is achieved through three steps and using complementary soft computing methods. In the first step, the fuzzy system is automatically generated from the data using a fuzzy clustering method. A genetic algorithm is used to increase the performance of the initial fuzzy inference system in the second phase. In the last phase a multi-objective genetic algorithm is applied to achieve two goals: to preserve the accuracy of the fuzzy model to a given value and to enhance the interpretability of the fuzzy model by reducing the fuzzy sets in the rule base. Two datasets from the UCI Machine Learning Repository are selected to evaluate the proposed method.

A Transparent Fuzzy Rule-Based Clinical Decision Support System for Heart Disease Diagnosis

Heart disease (HD) is a serious disease and its diagnosis at early stage remains a challenging task. A well-designed clinical decision support system (CDSS), however, that provides accurate and understandable decisions would effectively help the physician in making an early and appropriate diagnosis. In this study, a CDSS for HD diagnosis is proposed based on a genetic-fuzzy approach that considers both the transparency and accuracy of the system. Multi-objective genetic algorithm is applied to search for a small number of transparent fuzzy rules with high classification accuracy. The final fuzzy rules are formatted to be structured, informative and readable decisions that can be easily checked and understood by the physician. Furthermore, an Ensemble Classifier Strategy (ECS) is presented in order to enhance the diagnosis ability of our CDSS by supporting its decision, in the uncertain cases, by other well-known classifiers. The results show that the proposed method is able to offer humanly understandable rules with performance comparable to other benchmark classification methods.

Applied Data Mining Approach in Ubiquitous World of Air Transportation

Noise is a big problem for people living near airports, therefore the public, airport authorities and pilots are looking for ways to reduce the noise in the vicinity of populated areas. Optimal solution would be flight paths that are farthest from those areas, and worst paths are those, that just go above them. There are two classes of paths, namely optimal and non-optimal ones. This paper is going to use one of successfully used data mining techniques, namely neural network, which is capable of recognizing patterns. We used some coordinates of various flight paths as input for learning purposes of Neural Network, and defined two classes representing the optimal and non-optimal flight paths. The results have shown that this technique is well capable of recognizing the optimal and non-optimal flight paths. This technique can be used to reduce the noise.

Intelligent credit risk evaluation system using evolutionary-neuro-fuzzy scheme

Building an accurate credit scoring model is very important to predict effectively the creditworthiness of new customers. Neural networks and genetic algorithm are suitable for building highly predictive credit scoring model, but the lack of transparency of these methods is a major drawback. On the other hand the main advantage of fuzzy models is their ability to describe the behavior of systems with a series of linguistic humanly understandable rules. In this paper we develop an accurate as well as transparent credit scoring model based on the evolutionary-neuro-fuzzy method. Two datasets from the UCI machine learning repository are selected to evaluate the proposed method.