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ELEMENTS OF ARTIFICIAL INTELLECT BASED ON NEURON ANALYSIS THROUGH CLINICAL TRIALS STATISTICS AND VARIOUS MODELS OF MACHINE LEARNING
The detection of tumors, and more broadly, tissue anomalies, are of significant concern in the diagnosis of human tissue cancers. There is a pronounced need for methodologies that are preferably non-invasive, rapid, sensitive, specific, and cost-effective, to reduce the prolonged diagnostic processes associated with current practices. To enhance the sensitivity and specificity of emerging technologies in anomaly detection, machine learning algorithms have been integrated. Presently, such algorithms are incorporated into the majority of medical anomaly detection technologies. Machine learning algorithms, including Artificial Neural Networks (ANNs), have become useful in the field of spectroscopy (Peter Lasch et al. 2006; P. Lasch et al. 2018; Moein 2014; Shahid, Rappon, and Berta 2019; Amato et al. 2013; Ajam 2015; Nunes da Silva et al. 2018). The main objective of implementing machine learning in spectroscopy applications is to derive meaningful information and insights from spectral data. This includes classification, regression, and clustering tasks related to the identification of chemical compounds, determination of concentration levels, and extraction of spectral features. Machine learning algorithms can be sorted into three classes. The first class is supervised learning, which plays a significant role in analyzing and interpreting spectral data. Within this method, Support Vector Machines (SVM) is the major classifying spectral data with high dimensionality, while Random Forest algorithms offer high accuracy through ensemble learning,
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