Boosting Learning Algorithms for Chronic Diseases Prediction: A Review

Heart disease is one of the significant diseases which causes a huge number of deaths all over the world. Even medical specialists are facing difficulties for the proper diagnosis of heart disease which raises a need for a new classification scheme. But it becomes a crucial task for healthcare providers due to the rapid increase of medical data size every day. To resolve this, several machine learning algorithms are discussed in this paper, and these algorithms’ performance is measured by using different metrics like accuracy, precision, recall, and F1-score. But these algorithms are not acceptable for accurate prediction and diagnosis. To further improve the accuracy of classifiers, different ensemble methods were used because for any machine learning algorithm, accuracy is the main criteria to measure the performance. In this new methodology, the feature importance method is used as a pre-processing technique to get a minimum number of attributes rather than using all attributes in the dataset which has impact on the accuracy of classifiers. After that pre-processed data is trained by using various classifiers like linear regression, SVM, naïve Bayes, and decision tree, and then finally, three ensemble methods like bagging, AdaBoosting, and Gradient boosting are used to boost the performance of the classifiers. From the observations, the bagging ensemble algorithm elevated the highest accuracy, 80.21%, than the accuracy of other classifiers.

An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution

EROS: Ensemble rough subspaces

Accurate and efficient disease diagnosis plays a crucial role in healthcare. This study proposes a novel approach to enhance disease diagnosis performance by combining bagging and Teaching-Learning-Based Optimization (TLBO). The objective is to develop an optimized ensemble model that leverages the strengths of multiple base classifiers to improve diagnostic accuracy. The proposed methodology involves several key steps. TLBO optimization process is employed to dynamically select the most informative instances (bags) from the training data. The optimization process iteratively refines the bags by considering the fitness of each ensemble model constructed using different base classifiers. The soundness of an ensemble model is evaluated based on its accuracy in predicting the target variable. To further enhance the performance of the base classifiers, hyperparameter tuning using grid search is incorporated into the model training process. This ensures that each base classifier is optimized with the best set of hyperparameters, leading to more accurate predictions. The optimized bags are then used to train the base classifiers, which include Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Gaussian Naive Bayes (GNB). The base classifiers are combined into ensemble models using a Voting Classifier, allowing them to complement each other’s strengths and improve overall prediction performance. The results indicate that the TLBO-optimized ensemble models outperform individual base classifiers and traditional ensemble methods. The diversity among classifiers plays a crucial role in influencing the performance of ensemble models, especially in the context of disease diagnosis. Evaluating dissimilarity measures between classifiers becomes a key strategy in disease diagnosis. The dynamically selected bags contribute to improved accuracy, as they contain the most relevant instances for disease diagnosis and also explore computation time and diversity proposed ensemble approach on disease datasets.

In day today life, data is generated in massive amount with rapid growth in health care environment. The medical industries have large amount of data sets, for diagnosis purpose and maintain patient's records. The medical researches come with new treatments and medicine every day. But availability of medical datasets is often not balanced in their class labels. The performance of some existing method is poor on imbalanced dataset. So the prediction of disease from imbalanced data becomes difficult to handle. In this proposal Classifier ensemble method (Random Forest algorithm) can be used to overcome existing classifier techniques. Multiple classifier system is more accurate and robust than an existing classifier technique. The ensemble method proves to be very efficient in classification of records from available imbalanced healthcare patient data, as it involves the process of considering opinion from multiple base classifiers, as opposed to the single classifier method. This method gives a very accurate and precise inference, as unrelated data's are removed because of multiple base classifiers. The problems of healthcare dataset especially with some uncertainty can be predicted.

Data-driven decision model based on dynamical classifier selection

Many diseases are increasing day by day and it takes too much time to detect. In India after Covid-19 pandemic so many diseases have been spread their era. Like Liver Disease, Lung cancer and Brain Stroke. They are among us and lethal diseases which need to predict earlier or in initial stage. Machine Learning (ML) is the subset of Artificial intelligent which can imitate like human intelligence and it can process the large information. The classification or prediction of those diseases can be done by classifiers. The disease prediction is the method which can predict future of Liver diseases, Lung Cancer and Brain Stroke possibilities based on the collection of historical dataset. In this paper we will use Hybrid Ensemble Classifier Model (HECM) which is the combination of Supervised Classifiers like LightGBM, Random Forest, KNN used as Ensemble Classifier then output given to Voting classifier for final output. Accuracy and time will be calculate

Functional Trees are one type of multivariate trees. This work studies the performance of different ensemble methods (Bagging, Random Subspaces, AdaBoost, Rotation Forest) using three variants (multivariate internal nodes, multivariate leaves or both) of these trees as base classifiers. The best results, for all the ensemble methods, are obtained using Functional Trees with multivariate leaves and univariate internal nodes. The best overall configuration is obtained with Rotation Forest. Ensembles of Functional Trees are compared to ensembles of univariate Decision Trees, being the results favourable for the variant of Functional Trees with univariate internal nodes and multivariate leaves. Kappa-error diagrams are used to study the diversity and accuracy of the base classifiers.

Bagging (Bootstrap Aggregating) has been proved to be a useful, effective and simple ensemble learning methodology. In generic bagging methods, all the classifiers which are trained on the different training datasets created by bootstrap resampling original datasets would be seen as base classifiers and their results would be combined to compute final result. This paper proposed a novel ensemble model that refines the bagging algorithm with an optimization process. The optimization process mainly emphasizes on how to select the optimal classifiers according to the accuracy and diversity of the base classifiers. While the select classifiers constitute the final base classifiers. The empirical results reveal that the new model does outperform the original method in terms of learning accuracy and complexity.

Graph machine-learning (ML) methods have recently attracted great attention and have made significant progress in graph applications. To date, most graph ML approaches have been evaluated on social networks, but they have not been comprehensively reviewed in the health informatics domain. Herein, a review of graph ML methods and their applications in the disease prediction domain based on electronic health data is presented in this study from two levels: node classification and link prediction. Commonly used graph ML approaches for these two levels are shallow embedding and graph neural networks (GNN). This study performs comprehensive research to identify articles that applied or proposed graph ML models on disease prediction using electronic health data. We considered journals and conferences from four digital library databases (i.e., PubMed, Scopus, ACM digital library, and IEEEXplore). Based on the identified articles, we review the present status of and trends in graph ML approaches for disease prediction using electronic health data. Even though GNN-based models have achieved outstanding results compared with the traditional ML methods in a wide range of disease prediction tasks, they still confront interpretability and dynamic graph challenges. Though the disease prediction field using ML techniques is still emerging, GNN-based models have the potential to be an excellent approach for disease prediction, which can be used in medical diagnosis, treatment, and the prognosis of diseases.

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

We propose a local boosting method in classification problems borrowing from an idea of the local likelihood method. Our proposal, local boosting, includes a simple device for localization for computational feasibility. We proved the Bayes risk consistency of the local boosting in the framework of Probably approximately correct learning. Inspection of the proof provides a useful viewpoint for comparing ordinary boosting and local boosting with respect to the estimation error and the approximation error. Both boosting methods have the Bayes risk consistency if their approximation errors decrease to zero. Compared to ordinary boosting, local boosting may perform better by controlling the trade-off between the estimation error and the approximation error. Ordinary boosting with complicated base classifiers or other strong classification methods, including kernel machines, may have classification performance comparable to local boosting with simple base classifiers, for example, decision stumps. Local boosting, however, has an advantage with respect to interpretability. Local boosting with simple base classifiers offers a simple way to specify which features are informative and how their values contribute to a classification rule even though locally. Several numerical studies on real data sets confirm these advantages of local boosting.

Alzheimer's disease (AD), a psychiatric problem, availing between those who are 65 and older. Additionally, the disease's steady development of a variety of visible and invisible symptoms, such as irritability and aggression, has a substantial negative impact on a patient's overall quality of life. Although many treatments have been developed to help reduce its symptoms, AD has no known cure. As a result, the field of AD management is growing, and a comprehensive framework for the early detection of AD must be created. In this study, we created three classification models for predicting AD using machine learning and five models for predicting AD using ensemble learning. SVM, DTs, and RF are the three basis classifiers employed in the current work. Five ensemble classifiers, XGBoost, Voting Classifier, Extra Trees (ETs) Classifier, Gradient Boost, and AdaBoost, are then thoroughly compared. After thoroughly inspecting the dataset for outliers or other noise, a feature selection method known as PCA and several preprocessing techniques are used to lessen the issue of overfitting and performance enhancement. Additionally, this study utilised the longitudinal information from the OASIS website, which included 150 patients overall, 72 of whom were not demented and 78 of whom were. The RF model, which had an accuracy of 83.92% compared to the other two base classifiers, provided the best classification performance, while the ETs Classifier, an ensemble classifier, performed the best when compared to base and ensemble classifiers, with an accuracy of 86.60%.

With the increasingly available electronic health records (EHR), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e.g., symptoms, auxiliary examination results, etc.) to the estimated diseases for each patient. However, most of the current disease prediction models focus on the prediction of a single disease; in the medical field, a patient often suffers from multiple diseases (especially multiple chronic diseases) at the same time. Therefore, multi-disease prediction is of greater significance for patients’ early intervention and treatment, but there is no doubt that multi-disease prediction has higher requirements for data extraction ability and greater complexity of classification. In this paper, we propose a novel disease prediction model DLKN-MLC. The model extracts the information in EHR through deep learning combined with a disease knowledge network, quantifies the correlation between diseases through NodeRank, and completes multi-disease prediction. in addition, we distinguished the importance of common disease symptoms, occasional disease symptoms and auxiliary examination results in the process of disease diagnosis. In empirical and comparative experiments on real EHR datasets, the Hamming loss, one-error rate, ranking loss, average precision, and micro-F1 values of the DLKN-MLC model were 0.2624, 0.2136, 0.2190, 88.21%, and 87.86%, respectively, which were better compared with those from previous methods. Extensive experiments on a real-world EHR dataset have demonstrated the state-of-the-art performance of our proposed model.

Heart disease has long been a major threat to human health, causing about one-third of all deaths each year. Therefore, there is a need for accurate and effective prediction of cardiac disease using machine learning techniques. This paper uses data from 1,319 patients with heart disease and applies several machine-learning methods to study the relationship between a total of eight factors. The algorithms used in this paper include neural networks, decision trees, random forests, and so on. The best model was established by the random forest method, with an accuracy of 96.97%, precision of 97.03%, recall of 96.97%, and f1-Score of 0.9. It was found that troponin and CK-MB indicators had the highest influence weights, and the sum of the weights of each model accounted for more than 75% of the total weight, which demonstrated their significance in the prediction of heart disease, and the results could be utilized for the future prediction of heart disease. In addition, it also plays an essential role in the prediction of heart disease. To sum up, this is really important for the prediction of future heart disease.