There is a considerable rise in cardiovascular diseases in the world. It is pertinently essential to make cardiovascular prediction accurate to the maximum. A forecast based on machine learning techniques can be beneficial in detecting cardiovascular disease (CVD) with maximum precision and accuracy. The disease's effective prediction helps in early diagnosis, which cuts down the mortality rate. A health history and the causes of heart disease require the efficient detection and prediction of CVD. Data analytics is beneficial for making predictions based on a massive amount of data, and it aids health clinics in disease prognosis. Regularly, a large volume of patient-related data is maintained. The information gathered can be used to forecast the emergence of upcoming diseases. Our study presents a detailed comparative study of Cardiovascular Disease by comparing the various machine learning techniques mainly comprising of classification and predictive algorithms. The study shows an in-depth analysis of around forty-one papers related to cardiovascular disease by using machine learning techniques. This study evaluates the selected publications rigorously and identifies gaps in the available literature, making it useful for researchers to develop and apply in clinical fields, primarily on datasets related to heart disease. The current study will aid medical practitioners in predicting heart threats ahead of time, allowing them to take preventative measures.

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network‐based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well‐studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state‐of‐the‐art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network‐based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.

Alzheimer's disease, a debilitating neurodegenerative condition, poses a formidable challenge in healthcare, necessitating early and precise detection for effective intervention. This study delves into the realm of Alzheimer's detection, leveraging the prowess of deep learning. A novel convolutional neural network (CNN) model is proposed for AD detection. This model achieves a remarkable accuracy of 99.99% on the test dataset, outperforming established pre-trained models like ResNet50, DenseNet201, and VGG16. The outcomes distinctly highlight the CNN model's superior precision in AD identification, marking a watershed moment in neurodegenerative disease detection. The findings of this research have important implications for the development of a more accurate and sensitive diagnostic tool, which could lead to significant advancements in the early diagnosis and treatment of Alzheimer's disease. This research not only presents a novel diagnostic approach for AD but also demonstrates its resilience and potential for accurate classification of early Alzheimer's disease diagnosis. Request access from your librarian to read this chapter's full text.