Detecting Heart Attacks Using Learning Classifiers

Cardiovascular diseases (CVDs) have emerged as a critical global threat to human life. The diagnosis of these diseases presents a complex challenge, particularly for inexperienced doctors, as their symptoms can be mistaken for signs of aging or similar conditions. Early detection of heart disease can help prevent heart failure, making it crucial to develop effective diagnostic techniques. Machine Learning (ML) techniques have gained popularity among researchers for identifying new patients based on past data. While various forecasting techniques have been applied to different medical datasets, accurate detection of heart attacks in a timely manner remains elusive. This article presents a comprehensive comparative analysis of various ML techniques, including Decision Tree, Support Vector Machines, Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting, Multilayer Perceptron, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. These classifiers are implemented and evaluated in Python using data from over 300 patients obtained from the Kaggle cardiovascular repository in CSV format. The classifiers categorize patients into two groups: those with a heart attack and those without. Performance evaluation metrics such as recall, precision, accuracy, and the F1-measure are employed to assess the classifiers’ effectiveness. The results of this study highlight XGBoost classifier as a promising tool in the medical domain for accurate diagnosis, demonstrating the highest predictive accuracy (95.082%) with a calculation time of (0.07995 sec) on the dataset compared to other classifiers

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

Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

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