G. Purusothaman

Bio: G. Purusothaman is an academic researcher. The author has contributed to research in topics: Data analysis & Bar chart. The author has an hindex of 1, co-authored 1 publications receiving 59 citations.

A Survey of Data Mining Techniques on Risk Prediction: Heart Disease

Abstract: Comparison of classification techniques in Data mining to find the best technique for creating risk prediction model of heart disease at minimum effort. In Data mining, different methods used to find risk prediction of heart disease. There are two types of model used in analysis of data. First one is applying single model to various heart data and another one is applying combined model to the data. The combined model also known as hybrid model. This paper provides a quick and easy understanding of various prediction models in data mining and helps to find best model for further work. This is unique approach because various techniques listed and expressed in bar chart to understand accuracy level of each. These techniques are chosen based on their efficiency in the literature. In previous studies of different researcher expressed their effort on finding best approach for risk prediction model and here we found best model by comparing those researcher’s findings as survey. This survey helps to understand the recent techniques involved in risk prediction of heart disease at classification in data mining. Survey of relevant data mining techniques which are involved in risk prediction of heart disease provides best prediction model as hybrid approach comparing with single model approach.

Human heart disease prediction system using data mining techniques

Abstract: 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.

Association rule generation and classification with fuzzy influence rule based on information mass value

Abstract: The association rule based classification is imperative in the disease prediction owing to its high predictability. To deal with the sensitive data, we propose an algorithm using fuzzy inference set. The association rule mining is improved further by generating an associative rules for each item of the data set. The ranking of the item in the data set is based on the information mass value estimated. The mass value represents the depth of the item in the data set and its class. Selection of the certain item set is done based on the mass value of different associated items. According to the associative items selected, the association rule mining is performed. For each association rule generated, this method calculates the impact of each object from the rules based on how fuzzy rules are generated. Fuzzy impact rules indicate symptoms and diagnostic labels. A class of disease posses disease influence measure that predicts each class of disease has changed. The proposed algorithm improves the classification efficiency and reduces the error rates.

Hybrid Approach for Heart Disease Prediction Using Data Mining Techniques

Abstract: Heart disease is one of the significant reason of death and disability. The shortage of Doctors, experts and ignoring patient symptoms lead to big challenge that may cause death, disability to the patient. Therefore, we need expert system that serve as an analysis tool to discover hidden information and patterns in hear disease medical data. Data mining is a cognitive procedure of discovering the hidden approach patterns from large data set. The available massive data can used to extract useful information and relate all attributes to make a decision. Various techniques listed and tested here to understand the accuracy level of each. In previous studies, researchers expressed their effort on finding best prediction model. This paper proposes new heart disease prediction system that combine all techniques into one single algorithm, it called hybridization. The result confirm that accurate diagnose can be taken by using a combined model from all techniques.

Prediction of heart disease at early stage using data mining and big data analytics: A survey

Abstract: In this paper, the various technologies of data mining (DM) models for forecast of heart disease are discussed. Data mining plays an important role in building an intelligent model for medical systems to detect heart disease (HD) using data sets of the patients, which involves risk factor associated with heart disease. Medical practitioners can help the patients by predicting the heart disease before occurring. The large data available from medical diagnosis is analyzed by using data mining tools and useful information known as knowledge is extracted. Mining is a method of exploring massive sets of data to take out patterns which are hidden and previously unknown relationships and knowledge detection to help the better understanding of medical data to prevent heart disease. There are many DM techniques available namely Classification techniques involving Naive Bayes (NB), Decision tree (DT), Neural network (NN), Genetic algorithm (GA), Artificial intelligence (AI) and Clustering algorithms like K-NN, and Support vector machine (SVM). Several studies have been carried out for developing prediction model using individual technique and also by combining two or more techniques. This paper provides a quick and easy review and understanding of available prediction models using data mining from 2004 to 2016. The comparison shows the accuracy level of each model given by different researchers.

Applying Machine Learning Techniques for Predicting the Risk of Chronic Kidney Disease

Abstract: Objective: This paper aims at predicting the early detection of chronic kidney disease also known as chronic renal disease for diabetic patients with the help of machine learning methods and finally suggests a decision tree to arrive at concrete results with desirable accuracy by measuring its performance to its specification and sensitiveness. Methods: The behaviour of learning algorithms determined on a set of data mining indicators has a proportionate effect on the resulting models. Discovering the knowledge from wide databases is termed as Data mining. Besides studying the existing available Clinic Foundation Heart Disease dataset, 600 clinical records collected by us from a leading Chennai based diabetes research centre. We have tested the dataset for classification using Naive Bayes and Decision tree method. Findings: On comparing the classification algorithms with respect to Naive Bayes and Decision tree, we came to conclusion that the accuracy is up to 91% for Decision tree classification. Applications/Improvement: In order to increase the accuracy of the prediction result, we have utilized algorithms such as neural network and clustering data which greatly helped in our mission and also gave scope for future research.