Nowadays, health disease are increasing day by day due to life style, hereditary. Especially, heart disease has become more common these days, i.e. life of people is at risk. Each individual has different values for Blood pressure, cholesterol and pulse rate. But according to medically proven results the normal values of Blood pressure is 120/90, cholesterol is and pulse rate is 72. This paper gives the survey about different classification techniques used for predicting the risk level of each person based on age, gender, Blood pressure, cholesterol, pulse rate. The patient risk level is classified using datamining classification techniques such as Naive Bayes, KNN, Decision Tree Algorithm, Neural Network. etc., Accuracy of the risk level is high when using more number of attributes.

Human heart disease prediction system using data mining techniques

Machine learning-based coronary artery disease diagnosis: A comprehensive review.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework

The healthcare industry collects large amounts of Healthcare data, but unfortunately not all the data are mined which is required for discovering hidden patterns and effective decision making. We propose efficient genetic algorithm with the back propagation technique approach for heart disease prediction. This paper has analyzed prediction systems for Heart disease using more number of input attributes. The System uses medical terms such as Gender, blood pressure, cholesterol like13 attributes to predict the likelihood of patient getting a Heart disease.

Heart disease prediction using data mining techniques

Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach

The Healthcare industry generally clinical diagnosis is done mostly by doctor's expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

Review of heart disease prediction system using data mining and hybrid intelligent techniques

Cardiovascular disease remains the biggest cause of deaths worldwide and the Heart Disease Prediction at the early stage is importance. In this paper Supervised Learning Algorithm is adopted for heart disease prediction at the early stage using the patient's medical record is proposed and the results are compared with the known supervised classifier Support Vector Machine (SVM). The information in the patient record is classified using a Cascaded Neural Network (CNN) classifier. In the classification stage 13 attributes are given as input to the CNN classifier to determine the risk of heart disease. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients. The results show the CNN classifier can predict the likelihood of patients with heart disease in a more efficient way.

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Heart Disease Prediction System Using Supervised Learning Classifier

Cardiovascular disease remains the biggest cause of deaths worldwide. The percentage of premature death from this disease ranges from 4% in high income countries and 42 % in low income countries. This shows the importance of predicting heart disease at the early stage. In this paper, a new unsupervised classification system is adopted for heart attack prediction at the early stage using the patient’s medical record. The information in the patient record are preprocessed initially using data mining techniques and then the attributes are classified using a Fuzzy C means classifier. In the classification stage 13 attributes are given as input to the Fuzzy C Means (FCM) classifier to determine the risk of heart attack. FCM is an unsupervised clustering algorithm, which allows one piece of data to belong to two or more clusters. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients, which gives a classification accuracy of 92%. The result shows that the proposed clustering algorithm can predict the likelihood of patients getting a heart attack in a more efficient and cost effective way than the other well known algorithms.

/pdf/heart-attack-prediction-system-using-fuzzy-c-means-1s6gjayluc.pdf

Heart Attack Prediction System Using Fuzzy C Means Classifier

Myocardial Infarction (MI) also known as heart attack is one of the most dangerous cardiovascular diseases. Accurate early prediction can effectively reduce the mortality rate caused by MI. The early stages of MI may only have subtle indications which can be varied in variable risk factors and making diagnosis difficult even for experienced cardiologists. In this paper the computer aided detection system is proposed to find the risk level of MI using the supervised classifier. The MI prediction system is developed using Feed Forward Neural Network (FFNN), Cascade Correlation Neural Network (CNN), and Support Vector Machine (SVM). Genetic Optimized Neural Network (GAANN), Particle Swarm Optimized Neural Network (PSONN) and also the performance of the Computer Aided Detection system is analyzed using various performance metrics.

/pdf/myocardial-infarction-detection-using-intelligent-algorithms-3plm5bk9a7.pdf

Myocardial infarction detection using intelligent algorithms

Heart disease (HD) remains the biggest cause of deaths worldwide. This shows the importance of HD prediction at the early stage. In this paper, multi-layer feedforward neural network (MLFFNN) optimized with particle swarm optimization (PSO) is adopted for HD prediction at the early stage using the patientâ€™s medical record. The network parameters considered for optimization are the number of hidden neurons, momentum factor, and learning rate. The efficiency of the PSO optimized neural network (PSONN) is calculated using the records collected from standard Cleveland database and Real time clinical dataset. The results show the proposed system can predict the likelihood of HD patients in a more efficient and accurate way.

R. Chitra

Papers

Review of heart disease prediction system using data mining and hybrid intelligent techniques

Heart Disease Prediction System Using Supervised Learning Classifier

Heart Attack Prediction System Using Fuzzy C Means Classifier

Myocardial infarction detection using intelligent algorithms

Risk Prediction of Heart Disease Based on Swarm Optimized Neural Network