Ghazal, T M, Al Hamadi, H, Umar Nasir, M, Gollapalli, M, Zubair, M, Adnan Khan, M, Yeob Yeun, C and Gastaldo, P (2022) Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction. Computational Intelligence and Neuroscience, 2022. pp. 1-10. ISSN 1687-5265
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Abstract
Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.
Affiliation: | Skyline University College |
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SUC Author(s): | Ghazal, T M ORCID: https://orcid.org/0000-0003-0672-7924 |
All Author(s): | Ghazal, T M, Al Hamadi, H, Umar Nasir, M, Gollapalli, M, Zubair, M, Adnan Khan, M, Yeob Yeun, C and Gastaldo, P |
Item Type: | Article |
Uncontrolled Keywords: | Machine Learning, Multifactorial Genetic Inheritance Disorder |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr SUC Library |
Date Deposited: | 24 Jun 2022 12:31 |
Last Modified: | 24 Jun 2022 12:31 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/478 |
Publisher URL: | https://doi.org/10.1155/2022%2F1051388 |
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