###### Abstract  
Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator's skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000-1760 cm\({}^{-1}\)) and in the high-wavenumber range (2700-3700 cm\({}^{-1}\)). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection. color cells; FTIR spectroscopy; machine learning algorithms +
Footnote †: journal: Medical  
nucleic acids, proteins, and lipids [4; 5; 6], without requiring complex cell sample processing prior to measurements [7]. Indeed, FTIR microspectroscopy has largely been used to image and discriminate cancerous colon tissues from normal ones [8; 9; 10; 11; 12; 13; 14], whereas there are few FTIR investigations regarding cytological colon samples [15; 16]. Nonetheless, FTIR spectra measured for healthy and cancerous cells are quite similar to each other, because the spectral features related to specific biochemical components are only slightly modified by the onset of pathology. Thus, a simple visual observation of the measured spectrum of cytological samples in most cases cannot discriminate positive from negative outcomes. The comparison of the intensity values of specific absorption peaks from the spectra of different cell types is in many cases insufficient to obtain a reliable diagnosis. The problem with making a diagnostic evaluation via cell samples can be addressed by measuring the FTIR spectra of such samples. Moreover, mathematical models based on specific algorithms should be built in advance in order to properly diagnose the pathology according to the measured spectra: they are known as \"classification models\". In particular, the algorithms firstly operate on spectra of cellular samples whose classification (healthy, cancerous, metastatic, etc.) is known: these spectra are used to build classification models that will suitably allow for the classification of other unknown spectra. To build the classification models, the algorithms rely on the multivariate structure of the spectra that are provided to them. That is, instead of relying on the values of one or more specific variables (such as the absorption intensities at specific wavenumbers of the spectrum), they utilize a mathematical combination of several variables into new variables (often called \"latent variables\") that have a certain desired property which discriminates the spectra of cells belonging to different classes. Therefore, such latent variables can be used to predict this property for unknown spectra [17]. Machine learning algorithms are mechanisms that can learn the hidden patterns from input data (whose classes are known) and predict the output of new unknown data.