Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network

European Journal of Scientific Research
ISSN 1450-216X Vol.31 No.4 (2009), pp.642-656
© EuroJournals Publishing, Inc. 2009
http://www.eurojournals.com/ejsr.htm


Intelligent and Effective Heart Attack Prediction System Using
Data Mining and Artificial Neural Network


Shantakumar B.Patil
Ph.D. Scholar, Dr. MGR Educational and Research Institute, Chennai
E-mail: shantakumarpatilphd@gmail.com

Y.S.Kumaraswamy
Senior Professor &HOD, Department of MCA (VTU)
Dayananda Sagar College of Engineering, Bangalore, India


Abstract

The diagnosis of diseases is a vital and intricate job in medicine. The recognition of
heart disease from diverse features or signs is a multi-layered problem that is not free from
false assumptions and is frequently accompanied by impulsive effects. Thus the attempt to
exploit knowledge and experience of several specialists and clinical screening data of
patients composed in databases to assist the diagnosis procedure is regarded as a valuable
option. This research work is the extension of our previous research with intelligent and
effective heart attack prediction system using neural network. A proficient methodology for
the extraction of significant patterns from the heart disease warehouses for heart attack
prediction has been presented. Initially, the data warehouse is pre-processed in order to
make it suitable for the mining process. Once the preprocessing gets over, the heart disease
warehouse is clustered with the aid of the K-means clustering algorithm, which will extract
the data appropriate to heart attack from the warehouse. Consequently the frequent patterns
applicable to heart disease are mined with the aid of the MAFIA algorithm from the data
extracted. In addition, the patterns vital to heart attack prediction are selected on basis of
the computed significant weightage. The neural network is trained with the selected
significant patterns for the effective prediction of heart attack. We have employed the
Multi-layer Perceptron Neural Network with Back-propagation as the training algorithm.
The results thus obtained have illustrated that the designed prediction system is capable of
predicting the heart attack effectively.


Keywords: Data Mining, Disease Diagnosis, Heart Disease, Pre-processing, Frequent
Patterns, MAFIA (MAximal Frequent Itemset Algorithm), Clustering, K-
Means, Significant Patterns, Multi-layer Perceptron Neural Network
(MLPNN), Back-propagation (BP).

1. Introduction
Recently, the requirement of effective recognition of information- contextual data - non obvious and
important for decision making, from a huge ensemble of data has been constantly rising. This is an
interactive and iterative process consisting of numerous subtasks and decisions and is called as
Knowledge Discovery from Data. The essential process of Knowledge Discovery is the conversion of

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data into knowledge in order to aid in decision making, referred to as Data Mining [2, 20]. Knowledge
discovery in databases comprises of several distinct clearly exemplified processes. The essential
process is that of data mining; the one that assists the identification of concealed yet valuable
knowledge from enormous databases. A broadly recognized formal definition of data mining is given
as “Data mining is the non trivial extraction of implicit previously unknown and potentially useful
information about data” [4]. Traditionally, the mined information is represented as a model of the
semantic structure of the dataset. It might be possible to employ the model in the prediction and
classification of new data [1].
A wide variety of areas including marketing, customer relationship management, engineering,
medicine, crime analysis, expert prediction, Web mining, and mobile computing, besides others utilize
Data mining [5]. Numerous fields associated with medical services like prediction of effectiveness of
surgical procedures, medical tests, medication, and the discovery of relationships among clinical and
diagnosis data as well employ Data Mining methodologies [3]. Providing precious services at
affordable costs is a major constraint encountered by the healthcare organizations (hospitals, medical
centers). Valuable quality service denotes the accurate diagnosis of patients and providing efficient
treatment.. Poor clinical decisions may lead to disasters and hence are seldom entertained. Besides, it is
essential that the hospitals decrease the cost of clinical test. Appropriate computer-based information
and/or decision support systems can aid in achieving clinical tests at a reduced cost [9].
Owing to the accessibility of integrated information through enormous patient repositories,
there is a swing in the insight of clinicians, patients and payers from qualitative visualization of clinical
data to demanding a finer quantitative analysis of information with the assistance of all supporting
clinical and imaging data. For example; now the physicians can evaluate diagnostic information of a
variety of patients with identical conditions. Similarly, they can as well verify their findings with the
conformity of physicians working on similar cases from all over the world [7]. Medical diagnosis is
regarded as an important yet complicated task that needs to be executed accurately and efficiently. The
automation of this system would be extremely advantageous. Regrettably all doctors do not possess
expertise in every sub specialty and moreover there is a shortage of resource persons at certain places.
Therefore, an automatic medical diagnosis system would probably be exceedingly beneficial by
bringing all of them together [11].
Medical history data comprises of a number of tests essential to diagnose a particular disease
[8]. Clinical databases are elements of the domain where the procedure of data mining has develop into
an inevitable aspect due to the gradual incline of medical and clinical research data. It is possible for
the healthcare industries to gain advantage of Data mining by employing the same as an intelligent
diagnostic tool. It is possible to acquire knowledge and information concerning a disease from the
patient specific stored measurements as far as medical data is concerned. Therefore, data mining has
developed into a vital domain in healthcare [6]. It is possible to predict the efficiency of medical
treatments by building the data mining applications. Data mining can deliver an assessment of which
courses of action prove effective [12] by comparing and evaluating causes, symptoms, and courses of
treatments. The real-life data mining applications are attractive since they provide data miners with
varied set of problems, time and again. Working on heart disease patients databases is one kind of a
real-life application. The detection of a disease from several factors or symptoms is a multi-layered
problem and might lead to false assumptions frequently associated with erratic effects. Therefore it
appears reasonable to try utilizing the knowledge and experience of several specialists collected in
databases towards assisting the diagnosis process [2], [10].
The researchers in the medical field identify and predict the diseases besides proffering
effective care for patients [2, 6, 43, 44, 13] with the aid of data mining techniques. The data mining
techniques have been utilized by a wide variety of works in the literature to diagnose various diseases
including: Diabetes, Hepatitis, Cancer, Heart diseases and the like [39, 40, 41, 42]. Information
associated with the disease, prevailing in the form of electronic clinical records, treatment information,
gene expressions, images and more; were employed in all these works. In the recent past, the data

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Shantakumar B.Patil and Y.S.Kumaraswamy
mining techniques were utilized by several authors to present diagnosis approaches for diverse types of
heart diseases [14, 9, 22, 23, 24, 25].
This research work is the extension of our previous work [48] with intelligent and effective
heart attack prediction system designed with the aid of neural network. In our previous work, we have
presented an efficient approach for extracting patterns, which are significant to heart attack, from the
heart disease data warehouses. In that we have utilized the data mining techniques: clustering and
frequent pattern mining. The heart disease data warehouse consists of mixed attributes containing both
the numerical and categorical data. These records are cleaned and filtered with the intention that the
irrelevant data from the warehouse would be removed before mining process occurs. Then clustering is
performed on the preprocessed data warehouse using K-means clustering algorithm with K value so as
to extract data relevant to heart attack. Subsequently the frequent patterns significant to heart disease
diagnosis are mined from the extracted data using the MAFIA algorithm. The significant weightage is
calculated for each frequent pattern using the approach proposed. Then the patterns with significant
weightage greater than a predefined threshold value are chosen. Afterwards, the neural network is
trained with the selected significant patterns in order to predict heart attack in an efficient manner. We
have employed the Multi-layer Perceptron neural network for the design of prediction system with
Back-propagation as training algorithm. The efficacy of the designed system in predicting the heart
attack is illustrated by the acquired results.
The remaining sections of the paper are organized as follows: In Section 2, a brief review of
some of the works on heart disease diagnosis is presented. An introduction about the heart disease and
its effects are given in Section 3. The extraction of significant patterns from heart disease data
warehouse is detailed in Section 4. The heart attack prediction system designed with the aid of
MLPNN is elucidated in Section 5. The experimental results are described in Section 6. The
conclusions are summed up in Section 7.


2. Review of Related Background Literature
Numerous works in literature related with heart disease diagnosis using data mining and artificial
intelligence techniques have motivated our work. Some of the works are discussed below:
A novel technique to develop the multi-parametric feature with linear and nonlinear
characteristics of HRV (Heart Rate Variability) was proposed by Heon Gyu Lee et al. [14]. Statistical
and classification techniques were utilized to develop the multi-parametric feature of HRV. Besides,
they have assessed the linear and the non-linear properties of HRV for three recumbent positions, to be
precise the supine, left lateral and right lateral position. Numerous experiments were conducted by
them on linear and nonlinear characteristics of HRV indices to assess several classifiers, e.g., Bayesian
classifiers [17], CMAR (Classification based on Multiple Association Rules) [16], C4.5 (Decision
Tree) [18] and SVM (Support Vector Machine) [15]. SVM surmounted the other classifiers.
A model Intelligent Heart Disease Prediction System (IHDPS) built with the aid of data mining
techniques like Decision Trees, Naïve Bayes and Neural Network was proposed by Sellappan
Palaniappan et al. [9]. The results illustrated the peculiar strength of each of the methodologies in
comprehending the objectives of the specified mining objectives. IHDPS was capable of answering
queries that the conventional decision support systems were not able to. It facilitated the establishment
of vital knowledge, e.g. patterns, relationships amid medical factors connected with heart disease.
IHDPS subsists well being web-based, user-friendly, scalable, reliable and expandable.
The prediction of Heart disease, Blood Pressure and Sugar with the aid of neural networks was
proposed by Niti Guru et al. [22]. Experiments were carried out on a sample database of patients’
records. The Neural Network is tested and trained with 13 input variables such as Age, Blood Pressure,
Angiography’s report and the like. The supervised network has been recommended for diagnosis of
heart diseases. Training was carried out with the aid of back-propagation algorithm. Whenever
unknown data was fed by the doctor, the system identified the unknown data from comparisons with
the trained data and generated a list of probable diseases that the patient is vulnerable to.

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The problem of identifying constrained association rules for heart disease prediction was
studied by Carlos Ordonez [23]. The assessed data set encompassed medical records of people having
heart disease with attributes for risk factors, heart perfusion measurements and artery narrowing. Three
constraints were introduced to decrease the number of patterns. First one necessitates the attributes to
appear on only one side of the rule. The second one segregates attributes into uninteresting groups. The
ultimate constraint restricts the number of attributes in a rule. Experiments illustrated that the
constraints reduced the number of discovered rules remarkably besides decreasing the running time.
Two groups of rules envisaged the presence or absence of heart disease in four specific heart arteries.
Data mining methods may aid the clinicians in the predication of the survival of patients and in
the adaptation of the practices consequently. The work of Franck Le Duff et al. [24] might be executed
for each medical procedure or medical problem and it would be feasible to build a decision tree rapidly
with the data of a service or a physician. Comparison of traditional analysis and data mining analysis
illustrated the contribution of the data mining method in the sorting of variables and concluded the
significance or the effect of the data and variables on the condition of the study. A chief drawback of
the process was knowledge acquisition and the need to collect adequate data to create an appropriate
model.
A novel heuristic for efficient computation of sparse kernel in SUPANOVA was proposed by
Boleslaw Szymanski et al. [25]. It was applied to a benchmark Boston housing market dataset and to
socially significant issue of enhancing the detection of heart diseases in the population with the aid of a
novel, non-invasive measurement of the heart activities on basis of magnetic field generated by the
human heart. 83.7% predictions on the results were correct thereby outperforming the results obtained
through Support Vector Machine and equivalent kernels. The spline kernel yielded equally good results
on the benchmark Boston housing market dataset.
In [11] Latha Parthiban et al. projected an approach on basis of coactive neuro-fuzzy inference
system (CANFIS) for prediction of heart disease. The CANFIS model diagnosed the presence of
disease by merging the neural network adaptive capabilities and the fuzzy logic qualitative approach
and further integrating with genetic algorithm. On the basis of the training performances and
classification accuracies, the performances of the CANFIS model were evaluated. The CANFIS model
is promising in the prediction of the heart disease as illustrated by the results.
In [29] Kiyong Noh et al. put forth a classification method for the extraction of multi-
parametric features by assessing HRV from ECG, data preprocessing and heart disease pattern. The
efficient FP-growth method was the basis of this method which is an associative. They presented a rule
cohesion measure that allows a strong push of pruning patterns in the pattern generating process as the
volume of patterns created could possibly be huge. The multiple rules and pruning, biased confidence
(or cohesion measure) and dataset consisting of 670 participants, distributed into two groups, namely
normal people and patients with coronary artery disease, were employed to carry out the experiment
for the associative classifier.


3. Heart Disease
The term Heart disease encompasses the diverse diseases that affect the heart. Heart disease was the
major cause of casualties in the United States, England, Canada and Wales as in 2007. Heart disease
kills one person every 34 seconds in the United States [28]. Coronary heart disease, Cardiomyopathy
and Cardiovascular disease are some categories of heart diseases. The term “cardiovascular disease”
includes a wide range of conditions that affect the heart and the blood vessels and the manner in which
blood is pumped and circulated through the body. Cardiovascular disease (CVD) results in severe
illness, disability, and death [19]. Narrowing of the coronary arteries results in the reduction of blood
and oxygen supply to the heart and leads to the Coronary heart disease (CHD). Myocardial infarctions,
generally known as a heart attacks, and angina pectoris, or chest pain are encompassed in the CHD. A

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sudden blockage of a coronary artery, generally due to a blood clot results in a heart attack. Chest pains
arise when the blood received by the heart muscles is inadequate [17].
High blood pressure, coronary artery disease, valvular heart disease, stroke, or rheumatic
fever/rheumatic heart disease are the various forms of cardiovascular disease. The World Health
Organization has estimated that 12 million deaths occurs world wide, every year due to the
cardiovascular diseases. Half the deaths in the United States and other developed countries occur due
to cardio vascular diseases. It is also the chief reason of deaths in numerous developing countries. On
the whole, it is regarded as the primary reason behind deaths in adults [27].


4. Extraction of Significant Patterns from Heart Disease Data Warehouse
The extraction of significant patterns from the heart disease data warehouse is presented in this section.
The heart disease data warehouse contains the screening clinical data of heart patients. Initially, the
data warehouse is preprocessed to make the mining process more efficient. The preprocessed data
warehouse is then clustered using the K-means clustering algorithm with K=2. This result in two
clusters, one contains the data that are most relevant to heart attack and the other contains the
remaining data. The frequent patterns are mined from the data, relevant to heart attack, using the
MAFIA algorithm. The significant weightage is calculated for all frequent patterns with the aid of the
approach proposed. The frequent patterns with significant weightage greater than a predefined
threshold are chosen. These chosen significant patterns can be used in the design and development of
heart attack prediction system.

4.1 Data Preprocessing
Cleaning and filtering of the data might be necessarily carried out with respect to the data and data
mining algorithm employed so as to avoid the creation of deceptive or inappropriate rules or patterns
[33]. The actions comprised in the pre-processing of a data set are the removal of duplicate records,
normalizing the values used to represent information in the database, accounting for missing data
points and removing unneeded data fields. In order for making the data appropriate for the mining
process it needs to be transformed. The raw data is changed into data sets with a few appropriate
characteristics. Moreover it might be essential to combine the data so as to reduce the number of data
sets besides minimizing the memory and processing resources required by the data mining algorithm
[37]. In our approach, the heart disease data warehouse is refined by removing duplicate records and
supplying missing values. Furthermore it is also transformed to a form appropriate for clustering.

4.2 Clustering Using K-Means Algorithm
The categorization of objects into various groups or the partitioning of data set into subsets so that the
data in each of the subset share a general feature, frequently the proximity with regard to some defined
distance measure [31], is known as Clustering. The clustering problem has been addressed in numerous
contexts besides being proven beneficial in many applications. Clustering medical data into small yet
meaningful clusters can aid in the discovery of patterns by supporting the extraction of numerous
appropriate features from each of the clusters thereby introducing structure into the data and aiding the
application of conventional data mining techniques [32]. Numerous methods are available in the
literature for clustering. We have employed the renowned K-Means clustering algorithm in our
approach.
The k-means algorithm [38] is one of the widely recognized clustering tools that are applied in
a variety of scientific and industrial applications. K-means groups the data in accordance with their
characteristic values into K distinct clusters. Data categorized into the same cluster have identical
feature values. K, the positive integer denoting the number of clusters, needs to be provided in
advance.
The steps involved in a K-means algorithm are given subsequently:

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• K points denoting the data to be clustered are placed into the space. These points denote the
primary group centroids.
• The data are assigned to the group that is adjacent to the centroid.
• The positions of all the K centroids are recalculated as soon as all the data are assigned.
• Steps 2 and 3 are reiterated until the centroids stop moving any further. This results in the
segregation of data into groups from which the metric to be minimized can be deliberated.
The preprocessed heart disease data warehouse is clustered using the K-means algorithm with
K value as 2. One cluster consists of the data relevant to the heart disease and the other contains the
remaining data. Later on, the frequent patterns are mined from the cluster relevant to heart disease,
using the MAFIA algorithm.

4.3 Frequent Pattern Mining Using MAFIA
Frequent Itemset Mining (FIM) is considered to be one of the elemental data mining problems that
intends to discover groups of items or values or patterns that co-occur frequently in a dataset [26], [34].
It is of vital significance in a variety of Data Mining tasks that aim to mine interesting patterns from
databases, like association rules, correlations, sequences, episodes, classifiers, clusters and the like.
Numerous algorithms like the Apriori [21] and FP-Tree [30] have been proposed to support the
discovery of interesting patterns. The proposed approach utilizes an efficient algorithm called MAFIA
(MAximal Frequent Itemset Algorithm) which combines diverse old and new algorithmic ideas to form
a practical algorithm [35] [36]. The proposed algorithm is employed for the extraction of association
rules from the clustered dataset besides performing efficiently when the database consists of very long
itemsets specifically. The depth-first traversal of the itemset lattice and effective pruning mechanisms
are incorporated in the search strategy of the proposed algorithm.

Pseudo code for MAFIA [35]:
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
}
The cluster that contains data most relevant to heart attack is fed as input to MAFIA algorithm
to mine the frequent patterns present in it. Then the significance weightage of each pattern is calculated
using the approach described in the following subsection.

4.4 Significance Weightage Calculation
After mining the frequent patterns using MAFIA algorithm, the significance weightage of each pattern
is calculated. It is calculated based on the weightage of each attribute present in the pattern and the
frequency of each pattern. The formula used to determine the significant weightage (SW) is as follows:

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Shantakumar B.Patil and Y.S.Kumaraswamy
n
S
= ? W f
Wi
i
i
i =1
Where Wi represents the weightage of each attribute and fi denotes the frequency of each rule.
Subsequently the patterns having significant weightage greater than a predefined threshold are chosen
to aid the prediction of heart attack
SFP = {x : S (x) ? }
?
w

Where SFP represents significant frequent patterns and ? represents the significant weightage.
This SFP can be used in the design of heart attack prediction system.
5. Heart Attack Prediction System Using Neural Network
The design of the intelligent and effective heart attack prediction system with the aid of neural network
is presented in this section. The method primarily based on the information collected from precedent
experiences and from current circumstances, which visualizes something as it may occur in future, is
known as prediction. The degree of success differs every day, in the process of problem solving on
basis of prediction. Neural networks are one among the widely recognized Artificial Intelligence (AI)
machine learning models, and a great deal has already been written about them. A general conviction is
that the number of parameters in the network needs to be associated with the number of data points and
the expressive power of the network. The proposed word utilizes a multi-layer perceptron (MLP) with
back-propagation (BP) algorithm to train the selected significant patterns.

5.1. Multi-Layer Perceptron Neural Network (MLPNN)
Literature analysis unveils a persistent application of feed forward neural networks, from amidst the
various categories of connections for artificial neurons [47]. In feed-forward neural networks the
neurons of the first layer forward their output to the neurons of the second layer, in a unidirectional
fashion, which explains that the neurons are not received from the reverse direction. A kind of feed-
forward neural network mechanism is the Multi-layer Perceptron Neural Networks (MLPNN) or Multi-
layer feed-forward neural network (MFNN). The structure of MLPNN is shown in Figure 1.

Figure 1: Structure of MLPNN



A MLPNN can be described as a feed-forward artificial neural network model that is capable of
mapping sets of input data onto a set of appropriate output. It is an alteration of the typical linear
perceptron where in it employs three or more layers of neurons (nodes) with nonlinear activation
functions. The lone and primary task of the neurons in the input layer is the division of the input signal
x among neurons in the hidden layer. Every neuron j in the hidden layer adds up its input signals x
i
i

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once it weights them with the strengths of the respective connections w from the input layer and
ji
determines its output y as a function f of the sum, given as
j
y = f ?W X
j
( ji i)
At this instant it is possible for f to be a simple threshold function such as a sigmoid, or a
hyperbolic tangent function. The output of neurons in the output layer is determined in an identical
fashion.

5.2 Back-Propagation Training
The back-propagation algorithm can be employed effectively to train neural networks; it is widely
recognized for applications to layered feed-forward networks, or multi-layer perceptrons [46]. The BP
algorithm is capable of adjusting the network weights and biasing values to reduce the square sum of
the difference between the given output (X ) and an output values computed by the net (X ') with the
aid of gradient decent method as follows:
SSE = 1/
2 N?(X - )2
X'
Where N is the number of experimental data points utilized for the training.


6. Experimental Results
The results of our experimental analysis in finding significant patterns for heart attack prediction are
presented in this section. We have implemented our proposed approach in Java. In our previous work,
the heart attack dataset we have used for our experiments was obtained from [45]. With the help of the
dataset, the patterns significant to the heart attack prediction are extracted using the approach
discussed. The heart disease data set is preprocessed successfully by removing duplicate records and
supplying missing values. The refined heart disease data set, resultant from preprocessing, is then
clustered using K-means algorithm with K value as 2. Then the frequent patterns are mined efficiently
from the cluster relevant to heart disease, using the MAFIA algorithm. Subsequently, the significant
patterns are extracted with the aid of the significance weightage greater than the pre-defined threshold.
The values corresponding to each attribute in the significant patterns are as follows: blood pressure
range is greater than 140/90 mm Hg, cholestoral range is greater than 240 mg/dl, maximum heart rate
is greater than 100 beats/ minute, abnormal ECG and unstable angina.
In this work, in addition to these significant parameters, we have used some more parameters
significant to heart attack with their weightage and the priority levels are advised by the medical
experts. The neural network is trained with the selected significant patterns. The designed prediction
system employed MLPNN with Back-propagation as training algorithm. With the help of the designed
prediction system we can predict the different risk levels of heart attack.
The sample combinations of heart attack parameters for normal and risk level along with their
values and weightages are detailed below. Table. 1 shows the parameters of heart attack prediction
with corresponding values and their weightages. In that, lesser value (0.1) of weightage comprises the
normal level of prediction and higher values other than 0.1 comprise the higher risk levels. The
screenshots of heart attack prediction with different risk levels are shown in Figure 2, Figure 3, and
Figure 4.
If
Male And age < 30 And Smoking = Never And Overweight = No And Alcohol = Never And
Stress = No And High saturated fat diet (hsfd) = No And High salt diet (hsd) = No And
Exercise = Normal And Sedentary Lifestyle (Inactivity) = No And Hereditary = No And Bad
Cholesterol = Low And Blood Sugar = Normal And Blood Pressure = Normal And Heart Rate
= Normal

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Or
Male And age > 50 and age < 70 And Smoking = Current And Overweight = No And Alcohol
= Past And Stress = No And High saturated fat diet (hsfd) = No And High salt diet (hsd) =
Yes And Exercise = High And Sedentary Lifestyle (Inactivity) = No And Hereditary = No
And Bad Cholesterol = Low And Blood Sugar = Normal And Blood Pressure = Normal And
Heart Rate = Normal
Then
Risk Level = Normal
Otherwise If
Male And Age > 30 and age < 50 And Smoking = Current And Overweight = Yes And Alcohol
= Current And Stress = Yes And High saturated fat diet (hsfd) = No And High salt diet (hsd) =
Yes And Exercise = High And Sedentary Lifestyle (Inactivity) = Yes And Hereditary = Yes
And Bad Cholesterol = High And Blood Sugar = High And Blood Pressure = Low And Heart
Rate = Low Or High
Or
Female And Age >70 And Smoking = Never And Overweight = Yes And Alcohol = Past And
Stress = No And High saturated fat diet (hsfd) = Yes And High salt diet (hsd) = Yes And
Exercise = Never And Inactivity = No And Hereditary = Yes And Bad Cholesterol = High And
Blood Sugar = High And Blood Pressure = High And Heart Rate = High
Then
Risk Level = High

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Table 1:
Heart attack parameters with corresponding values and their weightages

Parameters Weightage

Age < 30
0.1
>30 to <50
0.3
Male and Female
Age>50 and Age <70
0.7
Age>70 0.8
Never 0.1
Smoking
Past 0.3
Current 0.6
Yes 0.8
Overweight
No 0.1
Never 0.1
Alcohol Intake
Past 0.3
Current 0.6
Yes 0.9
High salt diet
No 0.1
Yes 0.9
High saturated fat diet
No 0.1
Never 0.6
Regular 0.1
Exercise
High If age < 30
0.1
High If age > 50
0.6
Yes 0.7
Sedentary Lifestyle/inactivity
No 0.1
Yes 0.7
Hereditary
No 0.1
Very High >200
0.9
Bad cholesterol
High 160 to 200
0.8
Normal <160
0.1
Normal (130/89)
0.1
Blood Pressure
Low (< 119/79)
0.8
High (>200/160)
0.9
High (>120&<400)
0.5
Blood sugar
Normal (>90&<120)
0.1
Low ( <90)
0.4
Low (< 60bpm)
0.9
Heart Rate
Normal (60 to 100)
0.1
High (>100bpm)
0.9

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