## 8 Conclusions and Future Research  
The material presented in this paper shows a significant improvement in the accuracy of an ANN in classification problems with the aid of principal component analysis-sample reduction process (PCA-SRP). The ANN cast off 10% of the learning rate, two layers with 32 and 16 hidden neurons, ReLU activators in hidden layers, and SoftMax in output activators with 100 epochs or iterations, as shown in Table 3 based on the PCA-SRP and ANN The Python implementation program was implemented on the multidimensional datasets gathered, namely the heart disease, gender voice recognition, breast cancer classification, and cancer patients datasets provided in Table 4. These datasets were then used in the PCA-SRP + ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random samples testing; the results show significant improvements.  
PCA-SRP removed dirty and imprecise datasets, based on the results shown in Table 5, which allowed us to reduce the number of samples in the process and allowed for a significant increase in accuracy, as shown in Tables 6 and 7. Furthermore, we also determined the recommended \(Sc\) range values for normal cleaning and the ANN classification problem.  
Future research will further investigate the performance in massive biomedical datasets and determine how to load them into PCA-SRP cleansing agents; one of the suggestions is loading through batch processing. Another suggestion is to utilise knowledge-based techniques of PCA-SRP in different neural network architectures such as CNNs, RNNs, LSTMs, and GNNs. Furthermore, an investigation into various field applications to explore the incorporation of the investigated cleaning techniques, such as real-time biomedical automation, image-based medical diagnosis classification, and human thoughts processes [48; 49; 50; 51] could be a desirable research avenue.  
The proposed methodology can be applied to a wearable EEG or similar device in order to extract chaotic data from the brain's unique biometric featured samples for use in cryptography or especially for steganography. Lastly, this formal basis is necessary to design and construct high-quality and helpful software tools to support the data cleansing process of PCA-SRP and its application for artificial neural networks (ANNs).  
Conceptualisation, C.M.S.A., T.Y.W. and H.C.; methodology, C.M.S.A., T.Y.W. and H.C.; software, C.M.S.A.; validation, H.C., T.Y.W. and S.A.-M.; formal analysis, C.M.S.A. and H.C.; investigation, C.M.S.A., T.Y.W. and H.C.; resources, H.C., T.Y.W. and S.A.-M.; data curation, C.M.S.A.; writing--original draft preparation, C.M.S.A.; writing--review and editing, C.M.S.A.,  
Figure 27: +%Noise response for the cancer patients dataset (Sc = 98%).  
T.Y.W. and H.C.; visualisation, C.M.S.A., T.Y.W. and H.C.; supervision, T.Y.W. and H.C.; project administration, C.M.S.A. and H.C.; funding acquisition, C.M.S.A. and H.C. All authors have read and agreed to the published version of the manuscript.  
The publication of this article was funded by the Department of Science and Technology--Science Education Institute (DOST-SEI), Republic of the Philippines, through the Foreign Graduate Scholarship.  
The sample dataset is publicly available, but specific invisible health data will not be provided as consent was not given.  
The data presented in this study are available from the corresponding author upon request.  
The authors thank [PERSON] for their encouragement of the first author to perform research and for their proof reading of the English spelling and grammar in our research paper. Furthermore, we thank the faculty members of the University of Gloucestershire who supported the authors in their research.  
The authors declare no conflicts of interest.  
The sample datasets are available from the given URL in the references.  
The following abbreviations are used in this manuscript: