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Hybrid model for prediction of heart disease

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Abstract

Heart disease is a leading cause of death in the world. In order to drop its rate, effective and timely diagnosis of the disease is very essential. Numerous automated decision support systems have been developed for this purpose. In the present research, a predictive model consisting of two-level optimization is introduced, to save lives and cost via effective diagnosis of the disease. Level-1 optimization of the model first identifies parallelly an optimal proportion (Popt) for training and test sets for each dataset on parallel machine. Next, the best training set (Tbest) for Popt is again searched parallelly. On the other hand, level-2 optimization refines the rule set (R) generated by the Perfect Rule Induction by Sequential Method (PRISM) learner on Tbest employing parallel genetic algorithm. The experimental results obtained by the model over the heart disease datasets (collected from https://archive.ics.uci.edu/ml) are compared and analysed with its base learner and four state-of-the-art learners, namely C4.5 (decision tree-based classifier), Naïve Bayes, neural network and support vector machine. The empirical outcomes (based on the top performance metrics—prediction accuracy, precision, recall, area under curve values, true positive and false positive rates) positively demonstrate that the new model is proficient in undertaking heart disease treatment. Importantly, the prediction accuracy of the presented hybrid model exceeds around 6% than that of the sequential GA-based hybrid model over almost all the chosen datasets. After all, the proposed system may work as an e-doctor to predict heart attack and assist clinicians to take precautionary steps.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Bikash Kanti Sarkar

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  1. Bikash Kanti Sarkar
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Correspondence to Bikash Kanti Sarkar.

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Communicated by V. Loia.

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Appendices

Appendix A: Description Heart (Swiss) dataset

Classification dataset

The dataset for a classification problem is called as classification dataset. For better understanding about classification dataset, heart (Swiss) dataset with 13 non-target attributes (denoted as A1,…, A13) and its values (used in the present experiment) are described below.

  1. 1.

    Age (A1): age in years.

  2. 2.

    Sex (A2): sex (1 = male; 0 = female).

  3. 3.

    Cp (A3): chest pain type (4 types): Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain and Value 4: asymptomatic.

  4. 4.

    Trestbps (Resting blood pressure) (A4): in mm Hg on admission to the hospital.

  5. 5.

    Chol (A5): serum cholesterol in mg/dl,fbs.

  6. 6.

    Fbs (A6): (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false).

  7. 7.

    Restecg (A7): resting electrocardiographic results (of 3 types: Value 0: normal, Value 1: having ST-T wave abnormality (T-wave inversions and/or ST elevation or depression of > 0.05 mV), Value 2: showing probable or definite left ventricular hypertrophy by Estes’ criteria.

  8. 8.

    Thalach (A8): maximum heart rate achieved.

  9. 9.

    Exang (A9): exercise-induced angina (1 = yes; 0 = no).

  10. 10.

    Oldpeak (A10) = ST depression induced by exercise relative to rest.

  11. 11.

    Slope (A11): the slope of the peak exercise ST (T-wave) segment with values: Value 1: upsloping, Value 2: flat and Value 3: downsloping.

  12. 12.

    Ca (A12): number of major vessels (0-3) coloured by fluoroscopy.

  13. 13.

    Thalassemia (Thal) (A13): an inherited blood disorder: 3 = normal; 6 = fixed defect; 7 = reversible defect.

The num/goal (i.e. class denoted by C) field of each database refers to the presence and level or absence of heart disease in five forms (integer values: 0 to 4) in the patient, where the value 0 indicates the absence of heart disease (i.e. healthy) and 4 indicates severe. Rest of the values implies the presence of certain level of heart disease (i.e. unhealthy). Due to personal security, patients’ personal identification is replaced with dummy values (decided by the experts).

The dataset has total 123 instances in UCI data repository, and it is in non-discretized form. One part of the entire dataset is shown in Table 8.

Table 8 A part of the heart (Swiss) dataset (non-discretized form)
Full size table

A part of the discretized heart (Swiss) dataset (after preprocessed by MIL-discretizer) is shown in Table 9.

Table 9 A part of the discretized heart (Swiss) dataset
Full size table

Hybrid model

In data mining, a model that integrates the strengths of the data mining (base) approaches to enhance/improve the performance of the base approaches is called as hybrid model. Employing GA in hybrid system usually works to refine the solutions.

Imbalanced dataset

A dataset in which the number(s) of instances of some class(es) is/are very less in comparison with the instances of other classes is termed as imbalanced dataset. The instances of a class with very less in number are known as rare cases.

Appendix B: Rule generation by PRISM Learner

The PRISM learner generates ‘If–Then’ rules over the training dataset, and a sample rule set is shown below. In particular, the PRISM learner works as follows:

The attribute with maximum accuracy (a) = p/t, where p = number of instances (in the training dataset) for attribute value A = v for the target class and t = total number of instances (in the dataset) in which A = v (irrespective to any class value), is selected as a pre-condition for the current rule. Next, discard the examples covered by A = v from the training set (Fig. 6).

Fig. 6
figure 6

Flow diagram of PRISM learner

Full size image

2.1 A copy of ‘IF–Then’ rule set (R) generated by PRISM learner

  1. 1.

    If (A9 = 1) and (A13 = 1), then (C = 0).

[It may be read as: if (Exang (A9) = 1 (no)) and (Thalassemia (A13) = 1 (normal)), then heart disease is not present. Actually, the values of A9 and A13 here are the discretized/mapped values due to application of MIL-discretizer. For example, value 0 (before discretization) for A9 stands for no, but it is now 1. Likewise, earlier value 3 for A13 stands for normal, but it is now 1.]

  1. 2.

    If (A1 = 1) and (A2 = 1) and (A4 = 1), then (C = 1).

  2. 3.

    If (A2 = 2) and (A13 = 2), then (C = 1).

[If (sex (A2) = Male) and (Thalassemia (A13) = 2 (fixed defect)), then (heart disease is present with level-1)].

  1. 4.

    If (A5 = 1) and (A9 = 2) and (A10 = 5), then (C = 2).

  2. 5.

    If (A1 = 6) and (A9 = 2), then (C = 2).

  3. 6.

    If (A5 = 2) and (A9 = 2) and (A10 = 5), then (C = 3).

  4. 7.

    If (A9 = 2) and (A10 = 6), then (C = 4).

Appendix C: Interface method and its role

Role of the interface s/w

Let us take the above ‘If-Then’ rules as the input to the interface method which finally gives the rules in tabular structure as shown in (Table 10).

Table 10 Rule set (R) in I(R) form using interface software
Full size table

With respect to the proposed GA, Rule-1 and Rule-2 are shown in encoded format as follows:

  • Rule-1: 000000000000000000000000001000000000011000.

  • Rule-2: 001001000010000000000000000000000000000001.

Here, each binary block consists of 3 bits and each block represents one attribute (shown above in sequence). Last block represents the target attribute, whereas the rest blocks represent non-target attribute. ‘*’ (don’t care value) = 000.

Over the encoded rules, two-point crossover and then mutation operations are performed to generate new binary offspring which are decoded as per the suggested decoding scheme (discussed in Sect. 3.2.1) resulting in the decoded rules.

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Sarkar, B.K. Hybrid model for prediction of heart disease. Soft Comput 24, 1903–1925 (2020). https://doi.org/10.1007/s00500-019-04022-2

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