Data Mining - Concepts and Techniques.

C4.5: Programs for Machine Learning (書評)

In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of research. In recent years, significant attempts are made for the enhancement of computer aided diagnosis applications because errors in medical diagnostic systems can result in seriously misleading medical treatments. Machine learning is important in Computer Aided Diagnosis. After using an easy equation, objects such as organs may not be indicated accurately. So, pattern recognition fundamentally involves learning from examples. In the field of bio-medical, pattern recognition and machine learning promise the improved accuracy of perception and diagnosis of disease. They also promote the objectivity of decision-making process. For the analysis of high-dimensional and multimodal bio-medical data, machine learning offers a worthy approach for making classy and automatic algorithms. This survey paper provides the comparative analysis of different machine learning algorithms for diagnosis of different diseases such as heart disease, diabetes disease, liver disease, dengue disease and hepatitis disease. It brings attention towards the suite of machine learning algorithms and tools that are used for the analysis of diseases and decision-making process accordingly.

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Survey of Machine Learning Algorithms for Disease Diagnostic

For the past two decades, most of the people from developing countries are suffering from heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of death and also in reducing the cost of treatment. The objective of adaptive genetic algorithm with fuzzy logic (AGAFL) model is to predict heart disease which will help medical practitioners in diagnosing heart disease at early stages. The model consists of the rough sets based heart disease feature selection module and the fuzzy rule based classification module. The generated rules from fuzzy classifiers are optimized by applying the adaptive genetic algorithm. First, important features which effect heart disease are selected by rough set theory. The second step predicts the heart disease using the hybrid AGAFL classifier. The experimentation is performed on the publicly available UCI heart disease datasets. Thorough experimental analysis shows that our approach has outperformed current existing methods.

Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis

As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer-based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts' opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient's clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

Original Article: Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules

This work focuses on the problem of diagnosing the disease in the earlier stage by applying a selection technique based on genetic algorithm and least square support vector machines. The implementation of the technique analyses the accuracy of the classifier as well as the cost effectiveness in the implementation. This technique will help us to diagnose the disease with a limited number of tests that could be performed with minimal amount. We use evolutionary computation which is a subfield of artificial intelligence or computational intelligence that involves combinatorial optimization problems. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. The obtained results using the genetic algorithms approach show that the proposed method is able to find an appropriate feature subset and SVM classifier achieves better results than other methods.

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Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

This paper presents a genetic algorithm (GA)-based fuzzy logic approach for computer aided disease diagnosis scheme. The aim is to design a fuzzy expert system for heart disease diagnosis. The designed system is based on Cleveland Heart Disease database. Originally there were thirteen attributes involved in predicting the heart disease. In this work genetic algorithm is used to determine the attributes that contribute more towards the diagnosis. Thirteen attributes are reduced to six attributes using genetic search. Fuzzy expert system is used for developing knowledge based systems in medicine. The proposed system uses Mamdani inference method. The system designed in Matlab software can be viewed as an alternative for existing methods to distinguish of heart disease presence.

A Hybrid Genetic-Fuzzy Expert System for Effective Heart Disease Diagnosis

Time complexity analysis of genetic-fuzzy system for disease diagnosis. ABSTRACT A new generation of tools and techniques are needed for finding interesting patterns in the data and discovering useful knowledge. Especially, Medical knowledge consists of a combination of structural information about known biological facts and probabilistic or actuarial information about exposures to hazards and recovery rates. Probabilistic information is especially difficult to use, as it requires constant maintenance and it usually comes in the form of study results which are not ideally suited for making individual predictions. Patterns summarizing mutual associations between class decisions and attribute values in a pre-classified database provide insight into the significance of attributes and are also useful in classificatory knowledge. The proposed work is an efficient method to extract significant attributes from a database. Reducing the features or attributes enhances the quality of knowledge extracted and also the speed of computation. In this paper the design of a hybrid algorithm for heart disease diagnosis using effective and efficient genetic algorithm and fuzzy logic is implemented. The proposed work analyses the time complexity of genetic-fuzzy system.

http://www.airccse.org/journal/acij/papers/2411acij03.pdf

Time complexity analysis of genetic- fuzzy system for disease diagnosis

In this work, a framework that helps in the disease diagnosis process with big-data management and machine learning using rule based, instance based, statistical, neural network and support vector method is given. Concerning this, big-data that contains the details of various diseases are collected, preprocessed and managed for classification. Diagnosis is a day-to-day activity for the medical practitioners and is also a decision-making task that requires domain knowledge and expertise in the specific field. This framework suggests different machine learning methods to aid the practitioner to diagnose disease based on the best classifier that is identified in the health care system. The framework has three main segments like big-data management, machine learning and input/output details of the patient. It has been already proved in the literature that the computing methods do help in disease diagnosis, provided the data about that particular disease is available in the data center. Thus this framework will provide a source of confidence and satisfaction to the doctors, as the model generated is based on the accuracy of the classifier compared to other classifiers.

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Big data management with machine learning inscribed by domain knowledge for health care

Machine learning is the worldwide recent research technique for various systems as they are intelligent enough to find the solution for classification and prediction problems. The proposed work is about a hybrid genetic fuzzy algorithm that performs an optimal search as well as classification upon uncertain data. The data which is uncertain is suitable for fuzzy classifiers to predict the disease. The hybrid genetic fuzzy system applied on the attributes selects relevant attributes. The selected attributes are fed into the fuzzy classifier. The fuzzy rules are again generated using genetic algorithms. This algorithm is applied on three of the important and bench marking data sets taken from the UCI machine learning repository. The heart disease, Wisconsin breast cancer and Pima Indian diabetes datasets produce classification accuracy as 89.65%, 99.5% and 88.93% respectively. In this article there is a comparative study on few of the feature selection and feature reduction techniques.

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E. P. Ephzibah

Papers

Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

A Hybrid Genetic-Fuzzy Expert System for Effective Heart Disease Diagnosis

Time complexity analysis of genetic- fuzzy system for disease diagnosis

Big data management with machine learning inscribed by domain knowledge for health care

Comparative study on dimensionality reduction for disease diagnosis using fuzzy classifier