Supervised Machine Learning-Based Cardiovascular Disease Analysis and Prediction

Cardiovascular illness, often commonly known as heart disease, encompasses a variety of diseases that affect the heart and has been the leading cause of mortality globally in recent decades. It is associated with numerous risks for heart disease and a requirement of the moment to get accurate, trustworthy, and reasonable methods to establish an early diagnosis in order to accomplish early disease treatment. In the healthcare sector, data analysis is a widely utilized method for processing massive amounts of data. Researchers use a variety of statistical and machine learning methods to evaluate massive amounts of complicated medical data, assisting healthcare practitioners in predicting cardiac disease. This study covers many aspects of cardiac illness, as well as a model based on supervised learning techniques such as Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). It makes use of an existing dataset from the UCI Cleveland database of heart disease patients. There are 303 occurrences and 76 characteristics in the collection. Only 14 of these 76 characteristics are evaluated for testing, which is necessary to validate the performance of various methods. The purpose of this study is to forecast the likelihood of individuals getting heart disease. The findings show that logistic regression achieves the best accuracy score (92.10%).

Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method

Survival analysis concerns the analysis of time-to-event data and it is essential to study in fields such as oncology, the survival function, S(t), calculation is usually used, but in the presence of competing risks (presence of competing events), is necessary introduce other statistical concepts and methods, as is the Cumulative incidence function CI(t). This is defined as the proportion of subjects with an event time less than or equal to. The present study describe a methodology that enables to obtain numerically a shape of CI(t) curves and estimate the benefit time points (BTP) as the time (t) when a 90, 95 or 99% is reached for the maximum value of CI(t). Once you get the numerical function of CI(t), it can be projected for an infinite time, with all the limitations that it entails. To do this task the R function Weibull.cumulative.incidence() is proposed. In a first step these function transforms the survival function (S(t)) obtained using the Kaplan–Meier method to CI(t). In a second step the best fit function of CI(t) is calculated in order to estimate BTP using two procedures, 1) Parametric function: estimates a Weibull growth curve of 4 parameters by means a non-linear regression (nls) procedure or 2) Non parametric method: using Local Polynomial Regression (LPR) or LOESS fitting. Two examples are presented and developed using Weibull.cumulative.incidence() function in order to present the method. The methodology presented will be useful for performing better tracking of the evolution of the diseases (especially in the case of the presence of competitive risks), project time to infinity and it is possible that this methodology can help identify the causes of current trends in diseases like cancer. We think that BTP points can be important in large diseases like cardiac illness or cancer to seek the inflection point of the disease, treatment associate or speculate how is the course of the disease and change the treatments at those points. These points can be important to take medical decisions furthermore.

The Importance of Echocardiography Assessment in Coronary Patients Subject to Cardiovascular Recovery Programs

Cardiovascular diseases cause approximately one-third of deaths worldwide and an increasing number of individuals with non-fatal ischemic heart disease live with chronic disabilities and impaired quality of life. Cardiac rehabilitation is designed to limit the physiological and psychological effects of cardiac illness, reduce the risk for sudden death or re-infarction, control cardiac symptoms and enhance the psychosocial and vocational status of selected patients. The study group included a group of 78 patients who had a coronary event no more than 3 months ago and who are included in cardio-vascular recovery programs. The patients were echocardiographic evaluated at the first admission and later at 6 months. The evolution of the patients was a favorable one, being objectified an increase of both the ejection fraction of the left ventricle, as well as an improvement of MAPSE and TAPSE.

Improved Noise Cancelling Algorithm for Electrocardiogram Based on Moving Average Adaptive Filter

The electrocardiogram (ECG) is basic equipment used in the diagnosis of cardiac illness. However, in non-developed countries, most of the population does not have access to medical tests, and many hospitals do not even have these ECGs. On the other hand, the electrical signals generated by the heart and acquired by the ECG have low power and are affected by electromagnetic interference (EMI), mainly produced by the electrical system. Filtering EMI when frequency varies is a challenging task. Within this context, this work aims to produce an easy-to-use low-cost ECG with good electromagnetic disturbances rejection. The proposed noise rejection system is composed of two moving average filters and a phase-locked-loop, namely 2MAV-PLL. The system operates with a low sampling frequency and attenuates the EMI noise present in the ECG signal regardless of the amplitude, obtaining a filtered signal with a 44-dB signal–noise ratio (SNR) between the frequencies of± 10 Hz of the fundamental frequency. Simulation and experimental results prove that the ECG system can attenuate the EMI using relatively low sampling frequency, giving adequate information for health professionals to properly evaluate an electrocardiogram.

Sinus Node Dysfunction and Pulmonary Hypertension—“Double Trouble” with Lithium Therapy

Lithium is considered a gold standard drug for the management of bipolar disorder and is a widely used mood-stabilizing drug in psychiatry practice. However, its side effects are of important concern. The narrow therapeutic index of lithium predispose it to toxicity/side effects, but various case reports in literature have shown that adverse drug reactions can occur even in the therapeutic range. We present the case of a 56-year-old woman with no history of cardiac illness presenting with tachycardia-bradycardia syndrome along with moderate pulmonary hypertension. Patient reverted to sinus rhythm after withholding lithium therapy for 1 week while her mean pulmonary artery pressure remained the same at day 10 of drug withdrawal.

The “Spiked Helmet” Sign Associated with ST-Elevation Myocardial Infarction: A Case Report

Introduction: The “spiked helmet” sign was first described in 2011 by Littmann and Monroe in a case series of eight patients. This sign is characterized by an ST-elevation atypically with the upward shift starting before the onset of the QRS complex. Nowadays the sign is associated with critical non-cardiac illness. Case Report: An 84-year-old man with a history of three-vessel disease presented to the emergency department with intermittent pain in the upper abdomen. The electrocardiogram revealed the “spiked helmet” sign. After ruling out non-cardiac conditions the catherization lab was activated. The coronary angiography revealed an acute occlusion of the right coronary artery, which was balloon-dilated followed by angioplasty. The first 24 hours went uneventfully with resolution of the “spiked helmet” sign. On the second day, however, the patient died suddenly and unexpectedly. Conclusion: Despite the association with non-cardiac illness, the “spiked helmet” sign can be seen by an acute coronary artery occlusion as an ST-elevation myocardial infarction (STEMI). Reciprocal ST-depression in these cases should raise the suspicion of STEMI.

Applying Ensemble Learning Algorithm in Early Prognosis of Heart Illness

Medical diagnosis and treatment of diseases are the key elements of machine learning algorithms nowadays. To find similarities between various diseases, machine learning algorithms are used. Many people are now dying due to sudden heart attacks. Predicting and diagnosing heart disease is a daunting aspect faced by physicians and hospitals around the world. There is a need to foreknow whether or not a person is at risk of heart syndrome in advance, in order to minimize the number of deaths due to heart disease. In this field, machine learning algorithms play a very significant role. Many researchers are carrying out their research in this field to create software that can assist doctors to make decisions about cardiac illness prognosis. In this paper, Random Forest and AdaBoost ensemble Machine Learning Procedures are used in advance to predict heart disease. The datasets are handled in python programming by means of Anaconda Spyder IDE to validate the machine learning algorithm.

Continuous Spinal Anaesthesia for Intertrochanteric Femur Fracture in a Patient with Skeletal Dysplasia

The practice of continuous spinal anaesthesia is not common. Though underutilised, it offers significant advantage when compared to the single-shot technique nonetheless. Time and again, it has proven its worth in patients with advanced cardiac illness, spinal deformities, and obesity. We here successfully employed this neuraxial anaesthetic technique in a sixty-two-year-old male patient with skeletal dysplasia, who presented for surgical fixation of intertrochanteric fracture of the femur. With short stature, anticipated difficult airway, and poor pulmonary status complicating the anaesthetic plan, we opted for continuous spinal anaesthesia. The procedure was carried out uneventfully with 8 mg of hyperbaric bupivacaine used in titration to anaesthetic needs. Patients with skeletal dysplasia present with wide array of clinical conditions that pose a formidable challenge to anaesthesiologists. Continuous spinal anaesthesia can be safely practiced in such patients as it provides a titratable form of neuraxial blockade with reduced dose of local anaesthesia. This, in turn, ensures a predictable block and, thus, hemodynamic stability.

The Association Between Cardiac Illness-Related Distress and Partner Support: The Moderating Role of Dyadic Coping

Managing cardiac illness is not easy because it dramatically disrupts people’s daily life and both the patient and his/her spouse are at risk for experiencing distress, which, in turn, may affect the support provided by the partner as caregiver. The partner, in fact, is the main source of support, but his/her support may sometimes be inadequate. In addition, dyadic coping (i.e., the way partners cope together against stress and support each other in times of difficulty) could likely be a moderating factor. The main aim of the present study was to examine the role that dyadic coping (DC, in terms of positive, negative, and common dyadic coping responses) plays in moderating the link between patient and partner cardiac illness-related distress (in terms of anxiety and depression) and partner support (in terms of overprotection, hostility, and partner support for patient engagement). The study included 100 married couples faced with cardiac illness who completed a self-report questionnaire. We analyzed our data in PROCESS using multiple regressions in order to assess the moderating effects of DC responses in the relationship between the couple’s cardiac illness-related distress and partner support. With regard to patient distress, results showed that higher levels of patient anxiety and depression were linked with ineffective partner support (i.e., overprotection and hostility). With regard to partner distress, higher levels of partner depression were linked with hostility; higher levels of partner depression and anxiety were associated with less partner support for patient engagement. Moreover, the association between distress and partner support was moderated by the quality of DC. In particular, low positive DC represented a risk factor for both the patient and the partner during a cardiac illness, as low positive DC exacerbated the link between patient and partner distress and less effective partner support styles. Also, higher levels of negative DC were risky for couples: The association between distress and less adequate partner supportive behaviors was stronger in the case of higher negative DC. These results imply a need for psychosocial interventions for couples in cardiac illness, especially for couples lacking relational competences, such as positive dyadic coping.