8,222 research outputs found
Cyclin-dependent kinases as drug targets for cell growth and proliferation disorders. A role for systems biology approach in drug development. Part II - CDKs as drug targets in hypertrophic cell growth. Modelling of drugs targeting CDKs
Cyclin-dependent kinases (CDKs) are key regulators of cell growth and proliferation. Impaired regulation of their activity leads to various diseases such as cancer and heart hypertrophy. Consequently, a number of CDKs are considered as targets for drug discovery. We review the development of inhibitors of CDK2 as anti-cancer drugs in the first part of the paper and in the second part, respectively, the development of inhibitors of CDK9 as potential therapeutics for heart hypertrophy. We argue that the above diseases are systems biology, or network diseases. In order to fully understand the complexity of the cell growth and proliferation disorders, in addition to experimental sciences, a systems biology approach, involving mathematical and computational modelling ought to be employed
Heart Disease Detection using Vision-Based Transformer Models from ECG Images
Heart disease, also known as cardiovascular disease, is a prevalent and
critical medical condition characterized by the impairment of the heart and
blood vessels, leading to various complications such as coronary artery
disease, heart failure, and myocardial infarction. The timely and accurate
detection of heart disease is of paramount importance in clinical practice.
Early identification of individuals at risk enables proactive interventions,
preventive measures, and personalized treatment strategies to mitigate the
progression of the disease and reduce adverse outcomes. In recent years, the
field of heart disease detection has witnessed notable advancements due to the
integration of sophisticated technologies and computational approaches. These
include machine learning algorithms, data mining techniques, and predictive
modeling frameworks that leverage vast amounts of clinical and physiological
data to improve diagnostic accuracy and risk stratification. In this work, we
propose to detect heart disease from ECG images using cutting-edge
technologies, namely vision transformer models. These models are Google-Vit,
Microsoft-Beit, and Swin-Tiny. To the best of our knowledge, this is the
initial endeavor concentrating on the detection of heart diseases through
image-based ECG data by employing cuttingedge technologies namely, transformer
models. To demonstrate the contribution of the proposed framework, the
performance of vision transformer models are compared with state-of-the-art
studies. Experiment results show that the proposed framework exhibits
remarkable classification results
The Study and Efficacy of Conventional Machine Learning Strategies for Predicting Cardiovascular Disease
Regarding medical science, cardiovascular disease is the main cause of death. Testing patient samples for cardiac disease can save lives and lower mortality rates. During a subsequent visit, the right remedies should be outlined and prescribed. One of the most important factors in preemptive cardiac disease diagnosis is accuracy. Based on this factor, many research approaches were examined and compared. According to the analysis of these approaches, new procedures appear to be more advanced and reliable in detecting cardiac illness. A notation of the methods and their underlying themes and precision levels will be discussed. This paper surveys many models that use these methods and methodologies and evaluates their performance. Models created utilizing supervised learning methods, such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Decision Trees (DT), Random Forest (RF), and Logistic Regression Units, are highly valued by researchers. For benchmark datasets like the Cleveland or Kaggle, the methodologies are derived from data mining, machine learning, deep learning, and other related techniques and technologies. The accuracy of the provided methods is graphically demonstrated
Data Mining Application for Healthcare Sector: Predictive Analysis of Heart Attacks
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceCardiovascular diseases are the main cause of the number of deaths in the world, being the heart
disease the most killing one affecting more than 75% of individuals living in countries of low and middle
earnings. Considering all the consequences, firstly for the individual’s health, but also for the health
system and the cost of healthcare (for instance, treatments and medication), specifically for
cardiovascular diseases treatment, it has become extremely important the provision of quality services
by making use of preventive medicine, whose focus is identifying the disease risk, and then, applying
the right action in case of early signs. Therefore, by resorting to DM (Data Mining) and its techniques,
there is the ability to uncover patterns and relationships amongst the objects in healthcare data, giving
the potential to use it more efficiently, and to produce business intelligence and extract knowledge
that will be crucial for future answers about possible diseases and treatments on patients. Nowadays,
the concept of DM is already applied in medical information systems for clinical purposes such as
diagnosis and treatments, that by making use of predictive models can diagnose some group of
diseases, in this case, heart attacks.
The focus of this project consists on applying machine learning techniques to develop a predictive
model based on a real dataset, in order to detect through the analysis of patient’s data whether a
person can have a heart attack or not. At the end, the best model is found by comparing the different
algorithms used and assessing its results, and then, selecting the one with the best measures.
The correct identification of early cardiovascular problems signs through the analysis of patient data
can lead to the possible prevention of heart attacks, to the consequent reduction of complications and
secondary effects that the disease may bring, and most importantly, to the decrease on the number
of deaths in the future. Making use of Data Mining and analytics in healthcare will allow the analysis
of high volumes of data, the development of new predictive models, and the understanding of the
factors and variables that have the most influence and contribution for this disease, which people
should pay attention. Hence, this practical approach is an example of how predictive analytics can have
an important impact in the healthcare sector: through the collection of patient’s data, models learn
from it so that in the future they can predict new unknown cases of heart attacks with better
accuracies. In this way, it contributes to the creation of new models, to the tracking of patient’s health
data, to the improvement of medical decisions, to efficient and faster responses, and to the wellbeing
of the population that can be improved if diseases like this can be predicted and avoided. To conclude, this project aims to present and show how Data Mining techniques are applied in
healthcare and medicine, and how they contribute for the better knowledge of cardiovascular diseases
and for the support of important decisions that will influence the patient’s quality of life
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