A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction

People nowadays are engrossed in their daily routines, concentrating on their jobs and other responsibilities while ignoring their health. Because of their hurried lifestyles and disregard for their health, the number of people becoming ill grows daily. Furthermore, most of the population suffers from a disease such as cardiovascular disease. Cardiovascular disease kills 35% of the world's population, according to W.H.O. A person's life can be saved if a heart disease diagnosis is made early enough. Still, it can also be lost if the diagnosis is constructed incorrectly. Therefore, predicting heart disease will become increasingly relevant in the medical sector. The volume of data collected by the medical industry or hospitals, on the other hand, can be overwhelming at times. Time-series forecasting and processing using machine learning algorithms can help healthcare practitioners become more efficient. In this study, we discussed heart disease and its risk factors and machine learning techniques and compared various heart disease prediction algorithms. Predicting and assessing heart problems is the goal of this research

2ARTs – Decision Support System for Exercise and Diet Prescriptions in Cardiac Recovery Patients

The global health care system is faced with a variety of complicated challenges, ranging from limited access and increasing expenses to an aging population causing increased pressure on healthcare systems. Healthcare professionals are seeking alternative approaches to provide fair access and sustain high-quality care for everyone as a result of these challenges. Patients have historically been restricted from accessing essential healthcare services due to traditional barriers like geographic distance, financial and resource limitations. Innovative solutions to these problems are starting to take shape, thanks to the growth of eHealth platforms that use technology to improve patient care. Through a comprehensive study of existing solutions in the healthcare domain, particularly in cardiology, we identified the need for a Decision Support System (DSS) that would empower physicians with valuable insights and facilitate informed physical and diet prescribing practices into Cardiac Rehabilitation Programmes (CRPs). The major goal of 2ARTs’ project is to create and implement a cardiac rehabilitation platform into a hospital's infrastructure. A key aspect of this platform is the integration of a decision support system designed to provide physicians with valuable information when prescribing individualized treatment prescriptions for each patient, minimizing the potential of human error. The DSS uses algorithms and predictive models to classify patients into distinct groups based on their features and medical history. This classification provides critical insights and additional knowledge to doctors, allowing them to make informed judgments regarding the most effective treatment options for each patient's cardiac rehabilitation journey. By using the power of data-driven analytics and machine learning, the DSS enables doctors to better understand each patient's needs and personalize treatment actions accordingly. In order to achieve the best possible results aligned with the goals of the project, a variety of approaches based on comprehensive studies were explored, specifically feature selection and feature reduction methods, where their performance metrics were evaluated, seeking the most effective solution. It was through this thorough analysis that Principal Component Analysis (PCA) emerged as the standout choice. PCA not only demonstrated superior outcomes in evaluation metrics, but also showcased excellent compatibility with the selected clustering algorithm along with the best results after an expert analysis. Moreover, with the analysis of the data types and features the dataset had, the K-Means algorithm produced the best results and was more adaptable to our dataset. We were able to identify useful insights and patterns within the data by employing both PCA and K-Means, opening the way for more accurate and informed decision-making in the 2ARTs project

An Investigation on Disease Diagnosis and Prediction by Using Modified K-Mean clustering and Combined CNN and ELM Classification Techniques

Data analysis is important for managing a lot of knowledge in the healthcare industry. The older medical study favored prediction over processing and assimilating a massive volume of hospital data. The precise research of health data becomes advantageous for early disease identification and patient treatment as a result of the tremendous knowledge expansion in the biological and healthcare fields. But when there are gaps in the medical data, the accuracy suffers. The use of K-means algorithm is modest and efficient to perform. It is appropriate for processing vast quantities of continuous, high-dimensional numerical data. However, the number of clusters in the given dataset must be predetermined for this technique, and choosing the right K is frequently challenging. The cluster centers chosen in the first phase have an impact on the clustering results as well. To overcome this drawback in k-means to modify the initialization and centroid steps in classification technique with combining (Convolutional neural network) CNN and ELM (extreme learning machine) technique is used. To increase this work, disease risk prediction using repository dataset is proposed. We use different types of machine learning algorithm for predicting disease using structured data. The prediction accuracy of using proposed hybrid model is 99.8% which is more than SVM (support vector machine), KNN (k-nearest neighbors), AB (AdaBoost algorithm) and CKN-CNN (consensus K-nearest neighbor algorithm and convolution neural network)

Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning

Cardiovascular diseases (CVD) have recently outdid all other reasons of death universal in both developing and developed nations. Initial detection of cardiac conditions and continuing therapeutic supervision by experts can lower the death rate. However, accurate diagnosis of cardiac issues in all circumstances and 24-hour patient consultation by a doctor are still not feasible due to the increased intellect, effort, and expertise required. In this study, a basic concept for an Machine Learning (ML)-based heart disease prediction system was presented to identify impending heart disease using Machine Learning techniques. Despite the increasing number of empirical studies in this topic, particularly from underdeveloped countries, here lack many synthesised research articles in the field. In a time when the amount of data available is constantly increasing, predictive analytics has become more and more important as a tool for heart welfare services and human protection.  By utilising data collected from previous events to predict future patterns and outcomes, this state-of-the-art technology assists heart-care agencies in making more informed decisions about how to best serve their clients. However, as with any other data-driven technology, predictive analytics must be used appropriately to guarantee effective and ethical business operations. Healthcare forecasting has gained importance in recent years due to the growing popularity of AI (Artificial Intelligence) and ML (Machine Learning). In the healthcare sector, forecasting can also aid physicians in providing more precise and timely diagnoses. By anticipating likely medical events, medical staff can identify and treat individuals with greater efficiency and precision. This could lead to better patient outcomes and even cost savings.  These systems provide excellent therapeutic support and have the ability to diagnose illnesses by mimicking human cognition.  This study's included studies focus on forecasting the heart healthcare system (HHS) using machine learning algorithms. We implemented the system using the K-means Elbow technique for registration and notification, a decision tree for HHS, and MySQL for immunisation reminders

Machine Learning for Biosensors

Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and data analysis models. The potential benefits of machine learning in biosensors are discussed, including the ability to analyze large and complex data sets, to detect subtle changes in biomolecular interactions, and to provide real-time monitoring of biological processes. The challenges associated with the integration of machine learning and biosensors are also addressed, including data availability, sensor performance, and computational requirements. We further highlight the challenges and opportunities for the integration of machine learning and biosensors, including the development of portable and low-cost biosensors, and the use of machine learning algorithms for efficient data analysis. Finally, we provide an outlook on future trends and emerging technologies in the field, including the use of artificial intelligence and deep learning algorithms for biosensors, and the potential for creating a fully autonomous biosensing system

Acute myocardial infarction patient data to assess healthcare utilization and treatments.

The goal of this study is to use a data mining framework to assess the three main treatments for acute myocardial infarction: thrombolytic therapy, percutaneous coronary intervention (percutaneous angioplasty), and coronary artery bypass surgery. The need for a data mining framework in this study arises because of the use of real world data rather than highly clean and homogenous data found in most clinical trials and epidemiological studies. The assessment is based on determining a profile of patients undergoing an episode of acute myocardial infarction, determine resource utilization by treatment, and creating a model that predicts each treatment resource utilization and cost. Text Mining is used to find a subset of input attributes that characterize subjects who undergo the different treatments for acute myocardial infarction as well as distinct resource utilization profiles. Classical statistical methods are used to evaluate the results of text clustering. The features selected by supervised learning are used to build predictive models for resource utilization and are compared with those features selected by traditional statistical methods for a predictive model with the same outcome. Sequence analysis is used to determine the sequence of treatment of acute myocardial infarction. The resulting sequence is used to construct a probability tree that defines the basis for cost effectiveness analysis that compares acute myocardial infarction treatments. To determine effectiveness, survival analysis methodology is implemented to assess the occurrence of death during the hospitalization, the likelihood of a repeated episode of acute myocardial infarction, and the length of time between reoccurrence of an episode of acute myocardial infarction or the occurrence of death. The complexity of this study was mainly based on the data source used: administrative data from insurance claims. Such data source was not originally designed for the study of health outcomes or health resource utilization. However, by transforming record tables from many-to-many relations to one-to-one relations, they became useful in tracking the evolution of disease and disease outcomes. Also, by transforming tables from a wide-format to a long-format, the records became analyzable by many data mining algorithms. Moreover, this study contributed to field of applied mathematics and public health by implementing a sequence analysis on consecutive procedures to determine the sequence of events that describe the evolution of a hospitalization for acute myocardial infarction. This same data transformation and algorithm can be used in the study of rare diseases whose evolution is not well understood

Edge-Based Health Care Monitoring System: Ensemble of Classifier Based Model

Health Monitoring System (HMS) is an excellent tool that actually saves lives. It makes use of transmitters to gather information and transmits it wirelessly to a receiver. Essentially, it is much more practical than the large equipment that the majority of hospitals now employ and continuously checks a patient's health data 24/7. The primary goal of this research is to develop a three-layered Ensemble of Classifier model on Edge based Healthcare Monitoring System (ECEHMS) and Gauss Iterated Pelican Optimization Algorithm (GIPOA) including data collection layer, data analytics layer, and presentation layer. As per our ECEHMS-GIPOA, the healthcare dataset is collected from the UCI repository. The data analytics layer performs preprocessing, feature extraction, dimensionality reduction and classification. Data normalization will be done in preprocessing step. Statistical features (Min/Max, SD, Mean, Median), improved higher order statistical features (Skewness, Kurtosis, Entropy), and Technical indicator based features were extracted during Feature Extraction step. Improved Fuzzy C-means clustering (FCM) will be used for handling the Dimensionality reduction issue by clustering the appropriate feature set from the extracted features. Ensemble model is introduced to predict the disease stage that including the models like Deep Maxout Network (DMN), Improved Deep Belief Network (IDBN), and Recurrent Neural Network (RNN). Also, the enhancement in prediction/classification accuracy is assured via optimal training. For which, a GIPOA is introduced. Finally, ECEHMS-GIPOA performance is compared with other conventional approaches like ASO, BWO, SLO, SSO, FPA, and POA

A Deep Learning Approach to Integrate Medical Big Data for Improving Health Services in Indonesia

Medical Informatics to support health services in Indonesia is proposed in this paper. The focuses of paper to the analysis of Big Data for health care purposes with the aim of improving and developing clinical decision support systems (CDSS) or assessing medical data both for quality assurance and accessibility of health services. Electronic health records (EHR) are very rich in medical data sourced from patient. All the data can be aggregated to produce information, which includes medical history details such as, diagnostic tests, medicines and treatment plans, immunization records, allergies, radiological images, multivariate sensors device, laboratories, and test results. All the information will provide a valuable understanding of disease management system. In Indonesia country, with many rural areas with limited doctor it is an important case to investigate. Data mining about large-scale individuals and populations through EHRs can be combined with mobile networks and social media to inform about health and public policy. To support this research, many researchers have been applied the Deep Learning (DL) approach in data-mining problems related to health informatics. However, in practice, the use of DL is still questionable due to achieve optimal performance, relatively large data and resources are needed, given there are other learning algorithms that are relatively fast but produce close performance with fewer resources and parameterization, and have a better interpretability. In this paper, the advantage of Deep Learning to design medical informatics is described, due to such an approach is needed to make a good CDSS of health services