Jesmin Nahar

Association rule mining to detect factors which contribute to heart disease in males and females

This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms - Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.

Computational intelligence for heart disease diagnosis: A medical knowledge driven approach

This paper investigates a number of computational intelligence techniques in the detection of heart disease. Particularly, comparison of six well known classifiers for the well used Cleveland data is performed. Further, this paper highlights the potential of an expert judgment based (i.e., medical knowledge driven) feature selection process (termed as MFS), and compare against the generally employed computational intelligence based feature selection mechanism. Also, this article recognizes that the publicly available Cleveland data becomes imbalanced when considering binary classification. Performance of classifiers, and also the potential of MFS are investigated considering this imbalanced data issue. The experimental results demonstrate that the use of MFS noticeably improved the performance, especially in terms of accuracy, for most of the classifiers considered and for majority of the datasets (generated by converting the Cleveland dataset for binary classification). MFS combined with the computerized feature selection process (CFS) has also been investigated and showed encouraging results particularly for NaiveBayes, IBK and SMO. In summary, the medical knowledge based feature selection method has shown promise for use in heart disease diagnostics.

Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research.

Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach

Cancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer. To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.

Kernel-based naive bayes classifier for breast cancer prediction

The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis.

Significant cancer risk factor extraction: An association rule discovery approach

Cancer is the top most death threat for human life all over the world. Current research in the cancer area is still struggling to provide better support to a cancer patient. In this research our aim is to identify the significant risk factors for particular types of cancer. First, we constructed a risk factor data set through an extensive literature review of bladder, breast, cervical, lung, prostate and skin cancer. We further employed three association rule mining algorithms, apriori, predictive apriori and Tertius algorithm in order to discover most significant risk factors for particular types of cancer. Discovery risk factor has been identified to shows highest confidence values. We concluded that apriori indicates to be the best association rule-mining algorithm for significant risk factor discovery.

Early Breast Cancer Identification: Which Way to Go? Microarray or Image Based Computer Aided Diagnosis!

The goal of this research is to develop a computer aided diagnostic (CAD) system that can detect breast cancer in the early stage by using microarray and image data. We verified the performance of six well known classification algorithms with various performance matrices. Although we do not suggest a unique classifier algorithm for a CAD system, we do identify a number of algorithms whose performance is very promising. The algorithms performance was validated by 3 images dataset; two have been used for the first time in this experiment. Multidimensional image filtering is adopted for the final data extraction. The image data classification performance is compared with microarray data. Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach.

Brain Cancer Diagnosis-Association Rule Based Computational Intelligence Approach

This research employs a computational intelligence based approach to identify the risk factors of brain cancer. More specifically this research utilizes association rule mining techniques to determine the risk factors derived from the brain cancer literature. The research also develops a novel database extracting data from existing literature. Arguably, the outcomes may aid designing brain cancer diagnostic systems and contribute to early diagnosis of the mortal disease. Additionally, it demonstrates the effectiveness of computational intelligence techniques in cancer disease diagnosis. The benefits of this research could apply to different stakeholders such as the CAD (Computer aided diagnosis) designers and the data mining community, the medical field and in particular general practitioners as well as the general public.

Medical knowledge based data mining for cardiac stress test diagnostics

Though an important preliminary process to diagnose cardiac abnormality, cardiac stress test is often inaccurate and involves costly follow-up procedures. Data mining based approaches have potentials to reduce the uncertainties in this respect. However, the exploration on such data mining approaches is still limited. Further, traditionally employed data mining techniques like feature selection often ignore features which are medically important, and as a consequence the results are doubted by the medical practitioners. The objective of this research is to compare the effectiveness of Medical Feature Selection against the traditional Computer-Automated Feature Selection mechanism employed by data mining community for a real life stress test scenario. Considering six different classification algorithms, its noted that the medical knowledge motivated feature selection (MFS) improved performance of the classifiers in terms of majority of the measures, as compared to the computer-automated feature selection (CFS). While a unique classifier was not observed as the best technique, MFS had the most notable effect on Support Vector Machine (SMO) and Decision Tree (J48) for the dataset. The findings are expected to assist better computer-aided heart disease diagnostics, and is thus of interest to medical practitioners and researchers.