Integrating Multiple Techniques to Enhance Medical Data Classification

Prediction of the diseases are possible using medical diagnosis system. This type of health care model can be developed using soft computing techniques. Hybrid approaches of data classification and optimization algorithm increases data classification accuracy. This research proposed applications of Moth Flame optimization (MFO) and Fuzzy Min Max Neural Network (FMMNN) for the development of medical data classification system. Here MFO algorithm considers bulk of features from the disease dataset and produces optimized set of features based on fitness function. MFO is able to avoid local minima problem and this is the main cause behind production of optimal set of features. Optimized features are then passed to FMMNN for classification of malignant and benign cases. As classification is concerned, model experiment achieved 97.74% accuracy for Liver Disorders and 86.95 % accuracy for Pima Indian Diabetes dataset. Improving the medical data classification accuracy is directly related to attain good human health.

Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized state-of-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a clean-cut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

Class imbalance is a common problem in medical data classification where uneven prevalence of certain features or diseases causes the learners to be biased toward the majority class. Another common issue is ambiguous samples where multiple identical feature vectors are labelled with different classes. To mitigate these issues, previous works have employed clustering or data resampling techniques at the cost of possible information loss. In this paper, we propose a classification method called fuzzy clustering tree classifier (FCTC) which is a semi-supervised, tree-based extension of fuzzy C-means (FCM) to address class imbalance and ambiguous samples without requiring data resampling. Tested on 8 well-known medical datasets, the proposed method achieves high overall classification performance with the presence of class imbalance and ambiguous samples. In addition, the proposed method provides good interpretability as it enables generation of logical rules from the learned tree.

A hybrid intelligent system for medical data classification

Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification

The artificial neural network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. In this paper, a recently developed swarm intelligence algorithm named as marine predators algorithm (MPA) is applied as a learning algorithm to train the ANN for classification tasks. The classification tasks are carried out on 10 well-known medical datasets. A comparative study is conducted with the classical Levenberg–Marquardt (LM) and particle swarm optimizer (PSO) algorithms. The classification results are analysed using multi-criteria decision-making (MCDM) method. The results with analysis establish that MPA outperforms other algorithms.

Background: Medical data classification has become a hot research domain in data mining, but still it faces the increased classification accuracy issues. Methods/Statistical Analysis: Novel Hidden Markov Model based Support Vector Machine (HMM-SVM) is proposed to classify and predict Coronary Artery Disease (CAD). The features are extracted using HMM, and normalized using SVM. Feature Extraction assist the classification algorithm to get better results. HMM-SVM performs classification by extracting the features of Z-AlizadehSani dataset and finally selects the appropriate feature to perform classification. Findings: Z-AlizadehSani dataset holds 303 records with 4 different types of features, which are demographic, symptom and examination, ECG, and laboratory cum echo. For extracting these features and finding hidden information there exists no common algorithm. In HMM-SVM, HMM is applied to extract features by finding the hidden and previous stage values, and SVM is applied to perform classification on extracted features. To analyze the performance of HMM-SVM benchmark performance metrics are utilized. Discriminative performance results of internal validations are high in the task of binary classification (i.e., sensitivity- 98.2%; specificity-97.96%). False Positive Rate of HMM-SVM is entirely low (i.e.,1.87%) when comparing with previous algorithms. HMMSVM holds the classification accuracy as 98.02% and which is the better cum expected results towards the prediction of CAD. Novelty: Detailed analysis indicates HMM-SVM have better effects towards classifying and predicting CAD. Furthermore, care needs to be placed in adhering to ethical principles while utilizing the models that are automated. Future studies should make use of bio-inspired concepts to get even better results. Keywords: CAD; Classification; SVM; HMM; Markov

One of the emerging technologies, seeking significant attention in the research area is cloud computing. However, privacy is the major concern in the cloud, as it is essential to manage the confidentiality in the data shared. In the first work, the privacy preservation model was developed by newly designed Kronecker product based Bat algorithm. Here, the previous work is extended by developing the classification algorithm for classifying the privacy preserved database. Initially, the Kronecker product based Bat algorithm finds the privacy preserved database from the original medical data. Then, the ontology based features are extracted from the privacy preserved database and given to the data classifier. Here, a classifier, named Whale based Sine Cosine Algorithm with Support Vector Neural Network (WSCA-SVNN), is newly developed for the data classification. The proposed WSCA algorithm helps in optimally choosing the weights for SVNN classifier, and finally, the WSCA-SVNN classifier classifies the medical data. The simulation of the proposed privacy preserved data classification network is done by utilizing the heart disease database. The analysis shows that the proposed WSCA-SVNN classifier scheme achieved an accuracy value of 90.29% during medical data classification.

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability---plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM---CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM---CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability---plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM---CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM---CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

Hyperspectral imaging is a rising imaging modality in the field of medical applications, and the combination of both spectral and spatial information provides wealth information for cell classification. In this paper, deep convolutional neural network (CNN) is employed to achieve blood cell discrimination in medical hyperspectral images (MHSI). As a deep learning architecture, CNNs are expected to get more discriminative and semantic features, which effect classification accuracy to a certain extent. Experimental results based on two real medical hyperspectral image data sets demonstrate that cell classification using CNNs is effective. In addition, compared to traditional support vector machine (SVM), the proposed method, which jointly exploits spatial and spectral features, can achieve better classification performance, showcasing the CNN-based methods' tremendous potential for accurate medical hyperspectral data classification.

The ultimate aim of the proposed method is to establish a model for classification of medical data. Various methods have been generated to health related data to detect upcoming health fitness usage including detecting person's spending and illness related issues for diseased persons. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied for extracting useful information and to convert suitable sample from raw medical datasets. Here, orthogonal local preserving projection (OLPP) is used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the optimal classifier. In the optimal classifier, artificial bee colony algorithm will be used with neural network. The effectiveness of our proposed is measured in terms of accuracy, sensitivity and specificity. Here, Switzerland dataset achieves the maximum accuracy value 95.935%.

A hybrid ensemble-filter wrapper feature selection approach for medical data classification

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

Lung cancer remains one of the leading causes of death worldwide, with many sufferers unaware of their condition until it is too late for treatment. Therefore, high-accuracy prediction methods are urgently needed for early detection of lung cancer. This research uses the Random Forest algorithm, known for its excellent performance in medical data classification. In this study, modeling was optimized by implementing hyperparameter optimization using Optuna. The results of the generated model show an accuracy rate of 98.6%, which is highly significant in the context of early lung cancer detection. Additionally, this algorithm demonstrated 100% recall for the positive class and 97% for the negative class, indicating that the model is highly effective in identifying patients who truly have lung cancer. Another advantage of this model is seen in the AUC (Area Under the Curve) value reaching 1, indicating 100% accurate predictions. With these results, this research affirms the importance of using the Random Forest algorithm in developing early detection systems for lung cancer. This not only can improve treatment success rates but also significantly reduce mortality rates from lung cancer.

Anemia is a prevalent hematological condition marked by decreased hemoglobin concentration in the blood, which can lead to serious health complications if undetected. Although machine learning has shown potential in supporting early diagnosis, its effectiveness is often hindered by irrelevant or excessive features. This study investigates the impact of ANOVA and Chi-Square feature selection methods in improving the effectiveness of three distinct machine learning models algorithms, Naive Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) for anemia classification. Using a Kaggle dataset consisting of 15,300 instances and 25 features, the evaluation of each model was conducted with reference to its accuracy, precision, recall, and F1-score, both before and after applying feature selection. Experimental results show a substantial improvement in classification performance after feature selection, with the SVM + ANOVA combination achieving the highest accuracy of 94.61%. In contrast, models without feature selection performed below 90%, highlighting the need for appropriate feature reduction techniques. This study contributes a comparative analysis framework for medical data classification, emphasizing the role of statistical feature selection in optimizing model accuracy. Its novelty lies in demonstrating consistent performance improvement across algorithms using real-world anemia data and providing evidence that ANOVA and Chi-Square can significantly enhance model generalization in medical diagnostic contexts.