Review on Heart Disease Prediction System using Data Mining Techniques

Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper [10] and in [6]. So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease

Exploiting electronic health records for research on atrial fibrillation: risk factors, subtypes, and outcomes

BACKGROUND: Electronic health records (EHRs), collected on large populations in routine clinical care, may hold novel insights into the heart rhythm disorder atrial fibrillation (AF). AIM: To exploit EHRs to investigate, validate and extend evidence for AF risk factors, subtypes, and outcomes. METHODS: The CALIBER dataset (1997–2010) linking primary care, secondary care, and mortality records for a representative subset of the UK population was used (i) to model associations between cardiovascular disease (CVD) risk factors and incident AF, including AF with (AF+) and AF without (AF–) intercurrent CVD, (ii) to create EHR definitions for eight AF subtypes (structural, focal, polygenic, postoperative, valvular, monogenic, respiratory and AF in athletes) and (iii) to investigate stroke outcomes by CHA2DS2-VASc, sex, and warfarin use. RESULTS: Among 1,949,052 individuals, 50,097 developed incident AF: 12,652 (25.3%) with AF+ and 37,445 (74.7%) with AF–. Smoking (HR [95%CI] for AF+ vs. AF–: 1.66 [1.56,1.77] vs. 1.21 [1.16,1.25]), hypertension (2.19 [2.11,2.27] vs. 1.65 [1.62,1.69]), and diabetes (2.03 [1.94,2.12] vs. 1.45 [1.41,1.49]) showed consistent direct associations with AF+ and AF–, while heavy drinking (1.17 [0.81,1.67] vs. 1.99 [1.68,2.34]) and total cholesterol levels (0.99 [0.96,1.02] vs. 0.85 [0.84,0.87]) showed inconsistent associations with AF+ and AF–. EHR definitions for AF subtypes were created by combining 2813 diagnosis, medication, and procedure codes. There were 12,751 individuals with AF and valvular heart disease. Prosthetic replacements, mitral stenosis and aortic stenosis showed higher HR [95%CI] for stroke, thromboembolism and mortality (1.13 [1.02,1.24], 1.20 [1.05,1.36], and 1.27 [1.19,1.37] respectively). The net-clinical benefit (NCB [95%CI] per 100 person-years) of warfarin was shown from CHA2DS2-VASc≥2 in men (0.5 [0.1,0.9]) and CHA2DS2-VASc≥3 in women (1.5 [1.1,1.9]). CONCLUSION: AF is a heterogeneous condition associated with diverse disease mechanisms. EHRs can help refine understanding of risk factors, subtypes, and outcomes with relevance for clinical practice

Minimal Patients’ Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques

Background: Stress echocardiography (SE) is a well-established diagnostic tool in assessing patients with suspected coronary artery disease (CAD). Cardiovascular risk factors are used in the assessment of the probability of CAD. The link between the outcome of SE and patients’ variables including cardiovascular risk factors, current medication and anthropometric variables has not been widely investigated. Objective: This study aims to use Machine Learning (ML) to predict significant CAD defined by positive SE results in patients with chest pain based on patients’ anthropometrics, cardiovascular risk factors and medication as variables. Methods: A ML framework is proposed to automate the prediction of SE results. The proposed framework consists of four stages; feature extraction, pre-processing, feature selection and classification stage. A mutual information-based feature selection method was used to investigate the amount of information that each feature carries to define the positive outcome of SE. Two classification algorithms, Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, and Random Forest classifiers have been deployed. Data from 529 patients have been used to train and validate the proposed framework. Their mean age was 61 (±12 SD). The data consists of the anthropological data and cardiovascular risk factors such as gender, age, weight, family history, diabetes, smoking history, hypertension, hypercholesterolaemia, prior diagnosis of CAD and prescribed medications at the time of the test. The results of the SE were defined as outcome. A total of 82 patients had positive (abnormal) and 447 negative (normal) results, respectively. The proposed framework has been evaluated using the whole dataset including the cases with prior diagnosis of CAD. Five folds cross validation was used to validate the performance of the proposed framework. We also investigated the model in the subset of patients with no prior CAD. Results: The feature selection methods showed that prior diagnosis of CAD, sex, and prescribed medications such as angiotensin convertase enzyme inhibitor or angiotensin receptor blocker were the features that shared the most information about the outcome of SE. SVM classifiers showed the best trade-off between sensitivity and specificity and was achieved with three features. The best trade-off between sensitivity and specificity for the whole dataset accuracy was 66.63% with sensitivity and specificity 72.87%, and 67.67% respectively. However, for patients with no prior diagnosis of CAD only two features (sex and angiotensin convertase enzyme inhibitor or angiotensin receptor blocker use) were needed to achieve accuracy of 70.32% with sensitivity and specificity at 70.24%. Conclusions: This pilot study shows that ML can predict the outcome of SE in detecting significant CAD based on only a few features: patient prior cardiac history, gender, and prescribed medication. Further research recruiting higher number of patients who underwent SE could further improve the performance of the proposed algorithm with the potential of facilitating patient’s selection for early treatment / intervention with avoiding un-necessary downstream testing

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs

Automatic segmentation of whole-body bone scintigrams as a preprocessing step for computer assisted diagnostics

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor quality images and artifacts necessitate that algorithms use su±cient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. We present a robust knowledge based methodology for detecting reference points of the main skeletal regions that simultaneously processes anterior and posterior whole-body bone scintigrams. Expert knowledge is represented as a set of parameterized rules which are used to support standard image processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our knowledge based segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is used for automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians

Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters

OBJECTIVE: Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.APPROACH: To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.MAIN RESULTS: The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.SIGNIFICANCE: The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD