Survey on data mining techniques in heart disease prediction

Presented by

S. Sivagowry

Research scholar

Bharathidasan university,

Trichy

Under the Guidance of

Dr. M.Durai Raj,

Assistant Professor

School of Computer Science and Engineering,

Bharathidasan University,

Trichy

Data Mining• Exploration of large data sets to extract hidden and

previously unknown pattern, etc.,• Two tasks:

Predictive Tasks Descriptive Tasks

• Predictive tasks predict the value of specific attribute based on other attributeClassification, Regression and Deviation Deduction

Contd..• Descriptive Tasks

– Derive pattern that summarize the relationship between data– Clustering, Association rule Mining and Sequential Pattern

Discovery

• Steps in Data Mining

Data Cleaning, Data Integration, Data Selection, Data transformation, Data Mining, Pattern Evaluation and Knowledge Representation

Contd.. Medical Data mining

Involves lot of accuracy and uncertaintyQuality service at affordable cost is a major challengeData is massiveDecision based on doctor’s experience may fail in some

casesData Mining in health care – an intelligent diagnostic tool

Heart Disease

29.2% of death is due to Cardio Vascular DiseaseCVD – leading cause for death in developing countries.

Data sets

Contd… Collected from University of California, Irvine (UCI).Cleveland data set, Hungary data set, Switzerland data set,

Long beach and statlog data set76 attributes14 are used

Data Mining Techniques in Heart Disease Prediction

ClusteringClassificationRegressionAssociation Rule Mining

Data Mining and Association Rules Carlos Ordonez and et. Al.,[7] used a simple mapping

algorithm. Treats numerical or categorical attributes as uniform. Decision tree is incapable – it automatically split

numerical value. (Medical data are in numerical format ) Interpreting experimental result by D.T is difficult Clustering medical data deserves further research Justify the use of A.R in Medical data

Contd…Deepika [11] used Pruning Classification Association

Rule (PCAR).PCAR comes from Apriori algorithm.Deletes minimum frequency item with minimum

frequency item sets.Deletes infrequent item from item sets.Classifies item based on frequency of item sets and

discovers frequent item sets.

Data Mining and ClassificationUsha Rani[38], used ANN in heart disease using feed

forward and back propagation algorithm.Experiment by single and multi layered neural network

models.Parallelism is implemented to speed up learning process. Neural network provides satisfactory results

Contd….In [3], Classification is based on Supervised machine

learning Algorithm.Tanagara tool is used to classify dataEvaluation by using 10 fold cross validation.The performance is analysed based on accuracy and time

taken to build the model. Naïve bayes is the better algorithmThe table below shows the perfomance study of algorithm

Contd..Algorithm used

Accuracy Time taken

Naïve bayes 52.33% 609ms

Decision List 52% 719ms

KNN 45.67% 1000MS

Contd..In [24], novel neuro fuzzy techniques is used.Preprocess by using Genetic Algorithm(GA).A four layered fuzzy neural network is used.Radial Basic Function neural network is constructed with

5 input, training and normalization in hidden layer and output layer with 1 node.

In [25], Intelligent Heart Disease Prediction System (IHDPS )is proposed using Decision Tree, NB and Neural network.

NB is the most effective one.

Contd..In [1], GA is used to determine the number of attributes.NB, D.T., Classification by Clustering are compared. DT takes more time to build the model.NB performs consistently before and after reduction

of attributes.CVC is poor in performance

Contd.. In [30], k-means clustering algorithm is used.Maximal Frequent Item Set Algorithm (MAFIA) is used. Multilayer perception network and back propagation algorithm is used as

training algorithm. Pseudo code for MAFIA [29]:MAFIA(C, MFI, Boolean IsHUT) {

name HUT = C.head C.tail;

if HUT is in MFI

stop generation of children and return

Count all children, use PEP to trim the tail, and recorder by increasing support,

For each item i in C, trimmed_tail {

IsHUT = whether i is the first item in the tail

newNode = C I

MAFIA (newNode, MFI, IsHUT)}

if (IsHUT and all extensions are frequent)

Stop search and go back up subtree

If (C is a leaf and C.head is not in MFI)

Add C.head to MFI

}

Contd… In [35], Naïve Bayes is used for predicting Decision Support in heart

disease prediction System.NB is found to be best in heart disease prediction. It can be used as a tool for training nurses and medical students for

diagnosing. It provides new ways of understanding and exploring the data. In [6], NB Classification can be used as a best decision support system. In [10], hybridization is used to train the neural network using GA. Feed

forward and Back propagation is used as a learning algorithm.When two more attributes are added with existing attributes, Neural

Network shows better performance in both the cases.

Contd..RIPPER, SVM, Decision Tree and ANN are compared

based on Sensitivity, Specificity, Accuracy, Error Rate, TP AND FP Rate. [20]

SVM predicts with least error rate and higher accuracy.

DM with Fuzzy Logic reduces the number of attributes and number of tests for the patients.[21]

Data Mining and ClusteringK-means clustering algorithm is used for the prediction of the

heart disease[4].Euclidean distance formula is usedNB is slow and Neural network takes number of iterations.Performance of clustering and classification algorithm is

compared [28].NB predicts with highest accuracy than Clustering Algorithm.

CONCLUSIONClassification task plays a vital role when compared with

Clustering, Association Rule and Regression.In Classification, each techniques has its own merits and

demerits.Reduction of attributes is considered.Hybridization of Classification with Fuzzy Logic can predict

with highest accuracy.

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