Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.
Information and Communication Technology (ICT) has evolved tremendously in the recent years which made it possible to save huge volumes of information from different fields of engineering and medical applications. This information should be provided mandatorily, in terms of objects (patterns) and massive number of features so that all the aspects of the domain get characterized [
Traditionally, FS method includes four fundamental stages such as subset evaluation, subset generation, result validation, and stopping criterion [
The current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-Based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is utilized as a classifier to diagnose the disease. Moreover, the utilization of CMBO-FS technique helps in removing the unwanted features and boosts the classification accuracy. The experimental results of the analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset was investigated under different dimensions and the technique’s supremacy is established.
Khademi et al. [
Abdar et al. [
In this study, a novel BDC-CMBOAI technique is presented to determine the occurrence of diseases using biomedical data. The proposed BDC-CMBOAI technique involves different processes namely, pre-processing, feature subset selection using CMBO-FS technique, and RR-based classification. Moreover, the utilization of CMBO-FS technique helps in getting rid of unwanted features and boosts the classification accuracy.
CMBO is a population-based technique simulated by the natural phenomena in which the cat attacks the mouse while mouse gets away from haven. The search agent, from the presented technique, is separated into two sets of cats and mice which scan the problem search spaces in an arbitrary movement. The presented technique upgrades the members of the population through two stages. In primary stage, the progress of cats near mice is demonstrated while in secondary stage, the mice running away to haven so as to save their life is modeled. From a mathematical viewpoint, all the members of the population denote the presented solutions to the problem. In general, the member of the populations contribute certain values to the problem variables based on their place from search spaces. Therefore, all the members of the population have vector whose value defines the variable of the problem. The population of this technique is defined with the help of a matrix name called population matrix as shown in
where
where
where
where
At this point,
At this point,
Begin CMBO. |
Transfer function manner refers to the chances of different place vector elements in terms of
At the time of classification, RR model is applied to derive a meaningful subset of features. RR [
To additive nodes with activation function
The above formula is revised as follows.
At this point,
At this point,
is the lowest norm least square solution of
The process of RR is outlined in the subsequent steps.
The performance validation of the proposed BDC-CMBOAI technique was conducted using three benchmark medical datasets [
No. | Datasets | Classes | Instances | Features | Missing value |
---|---|---|---|---|---|
1 | Wisconsin breast cancer (Wisconsin) | 2 | 699 | 9 | Yes |
2 | Pima Indians diabetes (Pima) | 2 | 768 | 8 | No |
3 | Thyroid | 3 | 215 | 5 | No |
No. of CV-Runs | BDC-CMBOAI | FOA-SVM | PSO-SVM | Grid-SVM |
---|---|---|---|---|
Accuracy (%) | ||||
CV-Run 1 | 97.90 | 97.20 | 96.10 | 96.30 |
CV-Run 2 | 98.10 | 97.10 | 96.00 | 92.40 |
CV-Run 3 | 98.00 | 96.80 | 96.40 | 96.40 |
CV-Run 4 | 98.30 | 96.40 | 96.70 | 96.50 |
CV-Run 5 | 98.30 | 97.20 | 96.80 | 96.50 |
Computation Time (s) | ||||
CV-Run 1 | 3.77 | 90.16 | 5.77 | 41.80 |
CV-Run 2 | 4.07 | 91.11 | 5.77 | 33.27 |
CV-Run 3 | 3.87 | 90.16 | 5.77 | 39.91 |
CV-Run 4 | 5.12 | 60.77 | 6.72 | 38.01 |
CV-Run 5 | 4.27 | 36.11 | 5.77 | 34.22 |
A detailed CT analysis results accomplished by BDC-CMBOAI technique against existing techniques is shown in
No. of CV-Runs | BDC-CMBOAI | FOA-SVM | PSO-SVM | Grid-SVM |
---|---|---|---|---|
Accuracy (%) | ||||
CV-Run 1 | 78.12 | 77.40 | 76.73 | 75.92 |
CV-Run 2 | 78.22 | 77.74 | 77.12 | 75.68 |
CV-Run 3 | 78.03 | 77.26 | 76.88 | 76.35 |
CV-Run 4 | 78.46 | 77.50 | 76.78 | 76.40 |
CV-Run 5 | 78.75 | 77.60 | 76.49 | 76.11 |
Computation Time (s) | ||||
CV-Run 1 | 66.87 | 79.67 | 313.11 | 183.42 |
CV-Run 2 | 110.65 | 131.55 | 390.93 | 183.42 |
CV-Run 3 | 88.01 | 105.61 | 390.93 | 261.24 |
CV-Run 4 | 145.19 | 157.49 | 364.99 | 235.30 |
CV-Run 5 | 159.22 | 183.42 | 364.99 | 209.36 |
A brief CT analysis was conducted between BDC-CMBOAI technique and existing techniques and the results are shown in
No. of CV-Runs | BDC-CMBOAI | FOA-SVM | PSO-SVM | Grid-SVM |
---|---|---|---|---|
Accuracy (%) | ||||
CV-Run 1 | 97.28 | 96.71 | 94.78 | 93.12 |
CV-Run 2 | 97.67 | 95.83 | 93.52 | 95.26 |
CV-Run 3 | 97.48 | 97.12 | 96.14 | 93.86 |
CV-Run 4 | 97.86 | 95.76 | 95.40 | 94.81 |
CV-Run 5 | 97.62 | 97.07 | 95.78 | 95.78 |
Computation Time (s) | ||||
CV-Run 1 | 0.64 | 16.01 | 13.12 | 0.78 |
CV-Run 2 | 0.41 | 16.25 | 12.03 | 0.66 |
CV-Run 3 | 0.64 | 17.75 | 11.97 | 0.78 |
CV-Run 4 | 0.78 | 19.86 | 13.54 | 1.02 |
CV-Run 5 | 0.95 | 16.43 | 12.82 | 1.08 |
A detailed CT analysis was conducted between BDC-CMBOAI method against existing methods and the results are shown in
In this study, a novel BDC-CMBOAI technique is presented to determine the occurrence of diseases using biomedical data. The proposed BDC-CMBOAI technique involves different processes namely pre-processing, feature subset selection using CMBO-FS technique, and RR-based classification. Moreover, the utilization of CMBO-FS technique helps in removing unwanted features and boosts the classification accuracy. The experimental analysis results of BDC-CMBOAI technique on benchmark medical dataset were investigated under several aspects. The extensive comparative results established the enhanced outcomes of BDC-CMBOAI technique under different evaluation measures. Therefore, BDC-CMBOAI technique can be recognized as a novel approach for biomedical data classification. In future, outlier detection approaches can be utilized to design effective biomedical data classification processes.