IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions

Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset

Massive training artificial immune recognition system for lung nodules detection

In the early detection and diagnosis of lung nodule, computer aided detection (CAD) has become crucial to assist radiologists in interpreting medical images and decision making. However, some limitations have been found in the existing CAD algorithms for detecting lung nodules, such as imprecision classification due to inaccurate segmentation and lengthy computation time. In this research, Massive Training Artificial Immune Recognition System (MTAIRS) is proposed to detect lung nodules on Computed Tomography (CT) scans. MTAIRS is developed based on the pixel machine learning and artificial immune-based system-Artificial Immune Recognition System (AIRS). Two versions of proposed algorithms have been investigated in the study: MTAIRS 1 and MTAIRS 2. Since segmentation and feature calculation are not implemented in the pixel-based machine learning, the loss of information can be avoided during the data training in MTAIRS 1 and MTAIRS 2. The experiment and analysis find that MTAIRS 1 and MTAIRS 2 have successfully reduced the computation time and accomplished good accuracy in the detection of lung nodules on CT scans compared to other well-known pixel-based classification algorithms. Furthermore, MTAIRS 1 and MTAIRS 2 are investigated to improve their performance in eliminating the false positives. A weighted non-linear affinity function is employed in the training of MTAIRS 1 and MTAIRS 2 to replace Euclidean distance in affinity measurement. The enhanced algorithms named, E-MTAIRS 1 and E-MTAIRS 2 are capable to reduce the false positives in the non-nodule classification while maintaining the accuracy in nodule detection. In order to further provide comparative analysis of pixel-based classification algorithms in lung nodules detection, a pixel-based evaluation method of Kullback Leibler (KL) divergence is proposed in this study. Based on the pixel-based quantitative analysis, MTAIRS 1 performs better in the elimination of false positives, while MTAIRS 2 in lung nodules detection. The average detection accuracy for both MTAIRS algorithms is 95%

Medical Data Classification Using Similarity Measure of Fuzzy Soft Set Based Distance Measure

Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers

Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Implementation of a heart disease risk prediction model using machine learning

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset