Artificial intelligence (AI) has many branches according to the tasks to be performed, with machine learning (ML) being one of the most well-known branches that has gained prominence alongside the development of computer science. It focuses on developing systems and algorithms that automatically learn from data without explicit programming (Kolides et al., 2023; Dwivedi et al., 2021; Khudhair et al., 2024c). However, two main types of ML are categorized according to the problem to be solved: supervised learning and unsupervised learning. Supervised learning relies on having pre-labeled input data (denoted X) and the desired output (denoted y). This type of learning aims to understand the hidden relationship between inputs and outputs to predict new outcomes based on unseen (new) input data (Jiang et al., 2020; Beam and Zupancic, 2022; Zhao et al., 2024). On the other hand, unsupervised learning uses input data that is not pre-labeled (does not contain output y). Instead, an unsupervised learning model is applied to discover patterns and hidden relationships in the data autonomously based on the input data only (Watson, 2023; Molnar et al., 2020). Furthermore, there are other types of ML such as reinforcement learning (RL), which interact directly with the problem's environment to build policies that guide decision-making based on rewards and penalties obtained through trial and error (Gao and Schweidtmann, 2024; Mutar and Jawad, 2023; Kumar et al., 2023; Khudhair et al., 2024b).

In the early stages of AI, researchers focused on building systems (with minimal intelligence) capable of performing specific tasks using fixed rules (conditional and logical operations). As the field evolved, scientists realized that intelligent systems needed methods to learn from data, rather than relying on rigid rule-based methods with minimal capabilities (Grzybowski et al., 2024; Jabber et al., 2023). As a result, supervised learning algorithms, specifically classifiers, emerged as tools for learning systems to make predictions or decisions based on the available experiences. However, one of the most preeminent algorithms in supervised learning is artificial neural network (ANN), inspired by the fundamental concept of neurons in the human brain and how they are interconnected (Palanivinayagam et al., 2023). These networks are based on the concept of neurons, which are basic units in the brain that communicate with each other to perform processes such as thinking and learning (Schmidgall et al., 2024). The algorithm simulates the functions of brain cells by proposing multiple layers of artificial neurons (an input layer and an output layer). These neurons interact with each other using weights assigned to each connection, and the role of the algorithm is to optimize these weights to minimize the error resulting from interactions with the input data, thereby producing accurate outputs (Jumaah et al., 2024). Moreover, an older algorithm inspired by mathematics is the logistic regression (LR), which aims to find a perfect line that best fits the data points, minimizing the error between actual and predicted labels. These methods were used in statistical analyses before being adopted in ML (Jumin et al., 2020). The complexity of linear operations increased, leading to more sophisticated methods, such as support vector machine (SVM), where the main idea is to create clear boundaries between different data classes by maximizing the margin between them (Quan and Pu, 2022). Comprehensively, most of ML classifiers have drawn their inspirations from mathematical operations or nature (e.g., simulating the functioning of human brain cells) to create robust systems (classifiers) for solving complex problems. Current ML classifiers face multiple challenges related to performance, accuracy or loss, overfitting, and handling data with complex and non-linear patterns (Tufail et al., 2023; Khudhair et al., 2024a).

In this context, this paper proposes a new classifier called artificial liver classifier (ALC), inspired by the human liver's biological functions. Specifically, it draws on the detoxification function, highlighting its ability to process toxins and convert them into removable forms. Additionally, improvements have been made to FOX optimization algorithm (FOX), a state-of-the-art optimization algorithm, to enhance its performance and ensure compatibility with the proposed ALC. The research aims to bridge the gap in current ML's algorithms by combining the simplicity of mathematical design with solid performance by simulating the detoxification function in the human liver. Furthermore, the proposed classifier aims to improve classification performance by processing data dynamically, simulating the human liver's adaptive ability, enabling its application in fields requiring high-precision solutions and flexibility in dealing with different data patterns. The main challenge lies in transforming the liver's detoxification function into a simplified mathematical model that effectively incorporates properties such as repetition, interaction, and adaptation to the data (Tan et al., 2024). By comparing the proposed classifier with established ML classifiers, the study expects to improve the performance of ML, including increased computation speed, better handling of overfitting problems, and avoidance of excessive computational complexity. Additionally, this paper introduces a new concept for drawing inspiration from biological systems, opening up extensive opportunities for researchers to develop mathematical models based on other biological functions of the liver, such as filtering blood or amino acid regulation (Ishibashi et al., 2009). Moreover, it represents a starting point for interdisciplinary applications combining biology, mathematics, and AI, enhancing our understanding of incorporating natural processes into ML techniques to create efficient, reliable, and intelligent systems.

The proposed ALC has been evaluated using a variety of commonly used ML datasets, including Wine, Breast Cancer Wisconsin, Iris Flower, MNIST, and Voice Gender (Hoffmann et al., 2019), which are explained in detail in Section 4.1. This diversity in the datasets ensures extensive coverage of different data types, including text, images, and audio, and enables handling binary and multi-class classification problems (Seliya et al., 2021; Parimala and Muneeswari, 2023; Sidumo et al., 2022). The purpose of using these datasets is to conduct comprehensive tests to assess the performance of the proposed ALC and compare it with the established classifiers. The originality and contributions that distinguish this research are as follows:

This paper is structured as follows: Section 2 reviews the literature that has attempted to address classification issues across various data types. Section 3 provides an analytical overview of the human liver, focusing on detoxification function and the study's motivation. Section 4 present the used materials and the proposed methodology, including the improvement of classifier design and FOX training algorithm. Sections 5,6 cover the presentation and analysis of results, including comparisons with previous works. Finally, the study concludes with findings, recommendations, limitations, and future research directions in Section 7.

This section reviews the standard algorithms used in ML classification, with their practical applications across various datasets highlighted (Sarker, 2021). Additionally, recent studies in the field are discussed to identify existing challenges and to shed light on research gaps requiring further attention (Azevedo et al., 2024). Accordingly, the extent to which the proposed classifier can offer practical solutions to these gaps and contribute to the future advancement of the field will be investigated. However, Xiao et al. utilized 12 standard ML classifiers on the MNIST dataset, demonstrating its suitability as a benchmark for evaluating the proposed ALC. Their results identified the Support Vector Classifier (SVC) with a polynomial kernel (C = 100) as the best-performing model, achieving an accuracy of 0.978 (Xiao et al., 2017). This comparable result poses a challenge for the proposed ALC to surpass. Furthermore, the study (Cohen et al., 2017) employed online pseudo-inverse update method (OPIUM) to classify the MNIST dataset, achieving an accuracy of 0.9590. However, the author noted that these results do not represent cutting-edge methods but rather serve as an instructive baseline and a means of validating the dataset. This makes it feasible to compare the performance of the proposed ALC against OPIUM, as surpassing this baseline would demonstrate an improvement over existing methods. On the other hand, in a comparative study by Cortez et al., three classifiers—SVM, multiple regression (MR), and ANN—were evaluated on the Wine dataset. The SVM model demonstrated superior performance, achieving accuracies of 0.8900 for red wine and 0.8600 for white wine, outperforming the other methods with an average accuracy of 0.8790 (Cortez et al., 2009). Hence, the findings of Cortez et al. serve as a foundation for further advancements in ML applications, providing a basis for evaluating the proposed ALC.

Another study utilized a recursive recurrent neural network (RRNN) on Breast Cancer Wisconsin dataset. The results demonstrated that the proposed model achieved an accuracy of 0.9950 (Rajeswari and Sakthi Priya, 2025). Despite its outstanding performance, the computational demands of RRNN require substantial resources, which may limit their applicability in resource-constrained environments. Moreover, the study (Fan et al., 2024) presents a new classification model called CS3W-IFLMC. This model incorporates intuitionistic fuzzy (IF) and cost-sensitive three-way decisions (CS3WD) approaches, contributing to improved classification accuracy and reduced costs associated with incorrect decisions. The proposed model has been evaluated using 12 benchmark datasets, demonstrating superior performance compared to large margin distribution machine (LDM), FSVM, and SVM. However, the study remains limited in scope, as it focuses solely on binary classification tasks and does not extend to multi-class classification problems (Fan et al., 2024). Furthermore, in another study, the researchers examined gender classification (male or female) based on voice data using multi-layer perceptron (MLP). The findings showed that the MLP model outperformed several other methods, including LR, classification and regression tree (CART), random forest (RF), and SVM. The MLP achieved a classification accuracy of 0.9675. This study concluded that the proposed model demonstrates strong discriminative power between genders, which enhances its applicability in auditory data classification tasks (Buyukyilmaz and Cibikdiken, 2016).

The reviewed literature, highlights significant advancements in classification models, primarily focusing on improving performance and addressing computational challenges. However, several limitations and research gaps remain. One major issue is the reliance on computationally intensive methods, which can hinder applicability in resource-constrained environments. The absence of practical hyperparameter tuning or reduction mechanisms may also contribute to overfitting and computational inefficiencies. These limitations underscore the need for a new classifier to address such challenges. Hence, the proposed ALC should emphasize simplicity in design to ensure faster training time with lower cost.

The liver, as illustrated in Figure 1, is the largest internal organ in the human body and is vital in numerous complex physiological processes. It is located in the right upper quadrant of the abdominal cavity and consists of two primary lobes, the right and left, surrounded by a thin membrane known as the hepatic capsule (Moradi et al., 2020). Internally, the liver is composed of microscopic units called hepatic lobules. These hexagonal structures contain hepatic cells organized around a central vein. The lobules are permeated by a network of hepatic sinusoids, which are small channels through which blood flows, facilitating the exchange of oxygen and nutrients between the blood and hepatic cells (Kennedy et al., 2021). Furthermore, the liver receives blood from two sources, each contributing different functions. The oxygenated blood enters via the hepatic artery from the aorta, meeting the liver's energy demands. While, the portal vein delivers nutrient-rich and toxin-rich blood from the gastrointestinal tract and spleen (Schlegel et al., 2023). The blood from both sources mixes in the hepatic sinusoids, allowing the hepatic cells to perform metabolic and regulatory functions efficiently (Gibert-Ramos et al., 2021).

However, detoxification is one of the most important liver's functions, which removes toxins from the bloodstream (Donati et al., 2020). Detoxification occurs in two phases. In the phase I, hepatic enzymes known as cytochrome P450 chemically modify toxins through oxidation and reduction reactions, altering their structures to make them more reactive (Guengerich, 2020). In the phase II, the modified compounds are conjugated with water-soluble molecules such as sulfates or glucuronic acid, making them easier to excrete (Sun and Schanze, 2022). Finally, the toxins are either excreted via bile into the digestive tract or removed from the bloodstream by the kidneys (Zhang et al., 2020).

The complex biochemical system of the liver has inspired us to develop a new ML classifier known as ALC, modeled after the liver's detoxification mechanisms. The design of the proposed ALC was guided by an in-depth understanding of the liver's two primary detoxification phases—Cytochrome P450 enzymes and Conjugation pathways—where toxins are transformed into excretable compounds. The proposed ALC classify feature vectors effectively with minimum training time by simulating these phases using simple ML and optimization methods. This innovation marks a significant step forward, demonstrating how biological systems can inspire advanced computational models. It particularly encourages researchers in computer science to explore biological processes for developing intelligent ML models.

This section presents the standard datasets employed for evaluating the proposed ALC in the conducted experiments. Additionally, the architecture of the proposed ALC is provided, including mathematical equations, algorithms, and flowcharts. Furthermore, the section elaborates on the FOX, which serves as the learning algorithm for the proposed ALC, highlighting its improvements.

This section presents the performance results of the proposed ALC on multiple benchmark datasets, as described in Section 4.1. The experimental parameter settings were configured for each dataset as follows: 500 detoxification cycles, a detoxification power of 10, and dataset-specific numbers of lobules. Specifically, the number of lobules was set to 10 for Iris Flower and Breast Cancer Wisconsin, 15 for Wine and Voice Gender, and 50 for MNIST. The choice of these values was done through a systematic empirical search, where a predetermined set of possible values has been considered, a validation based approach used. Different values of p were searched over each dataset and the value that provided the maximum average classification accuracy on a held-out validation split was selected. This strategy makes sure that the hyperparameters being chosen are tuned in a reproducible and performance-based fashion. In order to have an efficient and fair assessment on the performance of the models, cross-validation was used on the basis of k. In particular, each of the datasets has been split into k equal size folds (10-folds were used in our experiments), and the test has been repeated on every fold. Mean results of each of the runs were used to compute final performance metrics. To facilitate later comparison and analysis, additional classifiers, including MLP, SVM, LR, and XGBoost (XGB), were executed on the same datasets. However, all experiments were conducted on an MSI GL63 8RD laptop equipped with an Intel^® Core™ i7-8750H × 12 processor and 32 GB of memory. This consistent setup ensured a robust evaluation of the proposed ALC alongside the other classifiers under the same conditions.

The results presented in Section 5.3, derived from experiments conducted on the datasets described in Section 4.1, highlight the superior performance of the proposed ALC compared to other classifiers. However, a more in-depth statistical analysis is necessary, particularly of the validation set results, as they are considered more reliable indicators of classifier performance due to being obtained from unseen data. The statistical analysis presented in Table 8 compare the performance of the proposed ALC with four classifiers—XGB, SVM, MLP, and LR—across the five datasets described in Section 4.1. The analysis focuses on four metrics: loss, accuracy, overfitting gap, and training time, with statistical significance determined using the Wilcoxon signed-rank test at a threshold of P-value < 0.05. The Wilcoxon signed-rank test is used to compare paired samples, particularly when data may not follow a normal distribution. It assesses whether the differences between paired observations are statistically significant (Hodges et al., 2022). Hence, this analysis results provide insights into the strengths of the proposed ALC in terms of its generalization, accuracy, and computational efficiency.

The loss metric, which is a primary indicator of classifier generalizability, demonstrates that the proposed ALC outperforms other classifiers in several datasets. Specifically, in the Iris Flower dataset, the proposed ALC showed statistically significant improvements in loss compared to SVM (P = 0.012), MLP (P = 0.006), and LR (P = 0.037), while its performance was comparable to XGB (P = 0.432), indicating XGB outperforms the proposed ALC. Similarly, in the Breast Cancer Wisconsin dataset, the proposed ALC showed significant improvements over XGB (P = 0.004), SVM (P = 0.001), MLP (P = 0.015), and LR (P = 0.000). In the Wine dataset, the proposed ALC demonstrated significant improvements compared to XGB (P = 0.022), MLP (P = 0.005), and LR (P = 0.031), but did not show statistically significant with SVM (P = 0.893). These trends were consistent in more complex datasets like Voice Gender and MNIST, where the proposed ALC achieved lower loss values compared to other classifiers in most cases (P < 0.05). The findings indicate that the proposed ALC offers better generalization across these datasets of varying complexity.

In terms of accuracy, the differences between the proposed ALC and other classifiers were generally less pronounced, as reflected by P-values exceeding 0.05 in most datasets. Notable exceptions include the Voice Gender dataset, where the proposed ALC significantly outperformed SVM (P = 0.002), and the Breast Cancer Wisconsin dataset, where the proposed ALC showed an advantage over XGB (P = 0.015). Furthermore, additional accuracy comparisons were conducted with other models discussed in the related work Section 2, as presented in Table 9, demonstrating the superiority of the proposed ALC. These results suggest that while accuracy remains an important metric, it may not always effectively differentiate the performance of classifiers, particularly when accuracy levels are already high across classifiers (Qu et al., 2022).

The overfitting gap metric, which evaluates the difference between the performance on training and validation folds, reveals that the proposed ALC demonstrates superior generalization. In most datasets, significant improvements were observed, such as in the Breast Cancer Wisconsin and MNIST datasets, where all P-values were < 0.05. In contrast, the overfitting gap in the Wine dataset showed inconsistent patterns, with P-values largely exceeding the significance threshold (P = 0.500). These results support the ability of the proposed ALC to reduce the risk of overfitting. Furthermore, the training time metric is used to measure the speed of classifiers. The statistical results suggest that the proposed ALC is competitive and efficient. The training time differed considerably (at least) in datasets that are smaller, including Iris Flower (P < 0.01), Breast Cancer Wisconsin (P = 0.002), Wine (P = 0.002), and voice Gender (P = 0.001). But on a bigger and more complicated dataset such as MNIST, the training time of the proposed ALC was similar to that of other classifiers (P > 0.90). The advocated ALC had no significant differences with XGB since it utilized tree-based models, which tend to have short processing times.

In conclusion, this paper suggests a novel supervised learning classifier, termed the artificial liver classifier (ALC), inspired by the human liver's detoxification function. The ALC is easy to implement, fast, and capable of reducing overfitting by simulating the detoxification function through straightforward mathematical operations. Furthermore, it introduces an improvement to the FOX optimization algorithm, referred to as IFOX, which is integrated with the ALC as training algorithm to optimize parameters effectively. Furthermore, the ALC was evaluated on five benchmark machine learning datasets: Iris Flower, Breast Cancer Wisconsin, Wine, Voice Gender, and MNIST. The empirical results demonstrated its superior performance compared to support vector machines, multilayer perceptron, logistic regression, XGBoost and other established classifiers. Despite these superiority, the ALC has limitations, such as longer training times on large datasets and slower convergence rates, which could be addressed in future work using methods like mini-batch training or parallel processing. Finally, this paper underscores the potential of biologically inspired models and encourages researchers to simulate natural functions to develop more efficient and powerful machine learning models.