Performances of several machine learning algorithms and of logistic regression to predict Fasciola hepática in cattle

Desempenho de vários algoritmos de aprendizado de máquina e regressão logística para prever Fasciola hepatica em bovinos

Abstract

The objective of this work was to compare the performances of logistic regression and machine learning algorithms to predict infection caused by Fasciola hepatica in cattle. A dataset on 30,151 bovines from Uruguay was used. Logistic regression (LR) and the algorithms k-nearest neighbor (KNN), classification and regression trees (CART), and random forest (RF) were compared. The interquartile range (IQR) and z-score were used to improve the classification and compared to each another. Sex, age, carcass conformation score, fat score, productive purpose, and carcass weight were used as independent variables for all algorithms. Infection by F. hepática was used as a binary dependent variable. The accuracies of LR, KNN, CART, and RF were 0.61, 0.57, 0.57, and 0.58, respectively. The variable importance of LR showed that adult cattle tended to be infected by F. hepatica. All models showed low accuracy, but LR successfully distinguished variables related to F. hepatica. Both the IQR and z-score show similar results in improving the classification metrics for the used dataset. In the dataset, data related to climate or factors such as body weight can improve the reliability of the model in future studies.

Index terms:
Fasciola hepatica ; classification; data mining; fluke; machine learning

Resumo

O objetivo deste trabalho foi comparar os desempenhos da regressão logística e de algoritmos de aprendizado de máquina para prever infecção por Fasciola hepatica em bovinos. Um conjunto de dados de 30.151 bovinos do Uruguai foi usado no estudo. Foram comparados a regressão logística (RL) e os algoritmos k-nearest neighbor (KNN), árvores de decisão (CART) e random forest (RF). O intervalo interquartil (IQR) e o escore-z foram usados para melhorar a classificação e comparados entre si. Sexo, idade, escore de conformação de carcaça, escore de gordura, propósito produtivo e peso da carcaça foram usados como variáveis independentes para todos os algoritmos. A infecção por F. hepatica foi usada como variável dependente binária. Os níveis de precisão de RL, KNN, CART e RF foram 0.61, 0.57, 0.57 e 0.58, respectivamente. A variável importância do modelo de RL mostrou que bovinos adultos tenderam à infecção por F. hepatica. Todos os modelos apresentaram baixa precisão, mas a RL distinguiu com sucesso as variáveis relacionadas a F. hepatica. Tanto o IQR quanto o escore-z mostram resultados semelhantes quanto à melhoria da métrica de classificação para o conjunto de dados utilizadoo. No conjunto de dados, dados relacionados ao clima ou a fatores como peso corporal, podem melhorar a confiabilidade do modelo em estudos futuros.

Termos para indexação:
Fasciola hepática ; classificação; verme trematódeo; aprendizado de máquina; mineração de dados

Introduction

Livestock diseases – together with associated treatment costs and decreased productivity – causes significant economic and physiological losses to farmers (Yadav et al., 2023).

Recently, Fasciola hepática has been recently reported as occurring in cattle of all continents except for Antarctica (Drescher et al., 2023). More than 70 countries have been affected by this problem (Centers for Disease Control and Prevention, 2016). A review carried out between 2000 and 2015 showed the prevalent countries for fasciolasis in cattle are as follows: 11 ones in Africa; 5, in Asia; 13, in the Americas; 2, in Australia/Oceania; and 11, in Europe (Mehmood et al., 2017).

Fasciolasis is an infectious disease that is devastating in livestocks such as cattle, sheep, goat, horse, rabbit, and camel (Charlier et al., 2014; Howell et al., 2015; Beesly et al., 2018; Costa et al., 2019). It is also known as a type of liver fluke. In cattle, this disease leads to a decrease of body weight, carcass weight, milk yield, and reproductive performance, causing the failure of organs (Rashid et al., 2019).

The infection is transmitted orally via the ingestion of metacercaria. The parasite depends on the presence of suitable intermediate hosts, such as snails. The young parasites penetrate the intestinal wall and progress to the liver. After residing in the bile ducts for a period of time, the adult parasite releases their eggs into the environment through feces. Symptoms manifest 11-12 weeks after infection. The life cycle of F. hepatica spans for about 18-24 weeks. (Kaplan, 2001; Urquhart et al., 2002; Balkaya et al., 2010).

Data mining, a.k.a. “knowledge mining from data”, have impacted many scientific fields. It has been used in many data sources as historical records (Ahmed, 2016), stock exchange (Patel et al., 2021), time series (Sabu & Kumar, 2020), biological sequence (Liao et al., 2018), sensors (Porto et al., 2015), spatial and geographical data (Ducheyne et al., 2015; Kaya et al., 2023), and social media (Zuliani et al., 2021). Big data provides massive information for machine learning algorithms, to develop predictive models (Zhou et al., 2017).

Advancements in machine learning have made possible to develop automated diagnostic technologies (Cihan et al., 2017). The use of big datasets has enabled the detection of potential diseases in livestock, and the development of early diagnosis approaches through machine learning algorithms (Ghosh & Dasgupta, 2022). Many studies have been published on animal science using such technology. These studies include the characterization of harmful bacteria in livestock (Hermann-Bank et al., 2015), identification of factors affecting pregnancy in cattle (Caraviello et al., 2006), classification of some cattle breeds based on morphological characteristics (Pares Casanova et al., 2012), detection of mastitis (Tanyildizi & Yildiπm, 2019; Altay & Delialioğlu, 2022), and the use of various algorithms to improve the accuracy of detecting bovine bluetongue disease (Gouda et al., 2022).

The objective of this work was to compare the performances of logistic regression and machine learning algorithms to predict infection caused by F. hepatica in cattle.

Materials and Methods

The open access data released by Corbellini et al. (2019) were used. This dataset consists of 30,151 rows, each one representing the individual record of bovines from a slaughterhouse in Uruguay. The columns of the dataset contain different variables such as date, sex, animal age (five categories), carcass weight (CW), carcass conformation code, carcass conformation score (CCS), fat score (FS) (fat coverage), productive purpose (PP), F. hepatica status, farm code, and department number. The variables date, carcass conformation code, farm code, and department number were not used in the present study. Detailed information on the variables employed in the present work is given (Table 1).

Table 1
Descriptive statistics of the independent variables according to the binary dependent variable.

Categorical variable	Category		Negative for F. hepatica		Positive for F. hepática
			n	(%)	n		(%)
Sex	Female		9,143	47.0	7,310		68.4
	Male		10,322	53.0	3,376		31.6
Age	0 (0–23 months)		598	3.1	146		1.4
	2 (23–30 months)		3,159	16.2	666		6.2
	4 (30–37 months)		3,471	17.8	1,020		9.5
	6 (37–42 months)		2,691	13.8	1,268		11.9
	8 (>42 months)		9,546	49.0	7,586		71.0
Carcass conformation score (CCS)	Low quality		641	3.3	755		7.1
	Regular and good quality		16,008	82.2	9,050		84.7
	Excellent quality		2,816	14.5	881		8.2
Fat score (FS) (Fat coverage)	Very low		1,047	5.4	1,013		9.5
	Low		4,655	23.9	2,079		19.5
	Regular		13,106	67.3	7,429		69.5
	Excessive		657	3.4	165		1.5
Productive purposes (PP)	Milk		5,050	25.9	2,251		21.1
	Beef (meat)		8,193	42.1	4,646		43.5
	Cross-breed		6,222	32.0	3,789		35.5
Continuous variable	Negative for F. hepatica				Positive for F. hepatica
	n	Min	Max	X±SX*	n	Min	Max	X±SX*
Carcass weight (CW)	19,465	81.5	601.6	258.6±0.32a	10,686	74.4	560.4	247.1±0.44b

• ^*
  Significant at 5% probability.

The statistical importance of the variables was analyzed using the chi-square test. All independent variables were significantly dependent on the binary dependent variable, at 5% probability. In data preparation, two different outlier detection techniques were performed, to determine observations which seemed inconsistent with the remainder of the dataset. Z-score transformation positively contribute to increase the accuracy of the classification models (Karo & Hendriyana, 2022). Therefore, z-score transformation was used to eliminate outliers in the dependent variable, according to following equation:

(1) $\text{Z}=\text{x}-\text{u}/\text{s}$

where: x is the current sample value; u is the overall mean of the sample; and s is the overall standard deviation of the sample.

Values out of the −3 <Z < +3 interval were considered outliers. The values of a total of 30,151 sample values were filtered down to 29,986, after the z-score transformation.

Quartiles are especially used, to avoid the effects of variation caused by outliers in the dataset (Sokal & Rohlf, 1969). In the present study, the first (Q₁) and third (Q₃) quartiles were used to detect outliers. Equations 2 and 3 give the values of the first and third quartiles, respectively.

(2) ${\text{Q}}_1=\left(\text{n}+1\right)/4$

(3) ${\text{Q}}_3=3\times \left(\text{n}+1\right)/4$

The interquartile range (IQR) value indicates the range where 50% of the data changes and can be calculated using the equation (4).

(4) $\text{IQR}={\text{Q}}_3-{\text{Q}}_1$

Outliers are then detected by equations 5 and 6.

(5) $\text{x}={\text{Q}}_1-1.5\times \text{IQ}$

(6) $\text{y}={\text{Q}}_3+1.5\times \text{IQ}$

Values smaller than x or larger than y, can be considered outliers. After outlier detection, 29,873 observations remained.

The total dataset was randomly separated into two sets: training (85%) and testing (15%). In literature, the ratios of the training and testing sets are usually 70% and 30%, respectively. In order to determine the accuracy of the model, the training set was chosen to be kept as high as possible. Therefore, instead of 70% of the dataset, 85% were selected for training. Five replicates and five-fold repeated cross validation were performed on the training dataset to tune hyperparameters. Detailed information on the tuning parameters was presented (Table 2).

Table 2
Tuning parameters used in algorithms.

Algorithms	Tuning parameters (z-score)	Tuning parameters (IQR)
KNN	k = 13	k = 13
CART (DT)	cp = 0.001881363	cp = 0.001583685
RF	mtry = 2 ntree = 500	mtry = 2 ntree = 500
LR	No need to tune	No need to tune

Due to the imbalance of the dataset for class variable, an undersampling strategy was performed. The downSample function in the caret package of the statistical software R was used for the undersampling of the classes. When the training dataset was split as 85% of the total dataset, the expected value of the dependent variable was determined to be “0”, for 16,453 observations, and it was determined to be “1” for 9,035 observations. Undersampling was applied to the training set by undersampling the majority class, without replacement in the class attribute.

Once data were properly adjusted, the models were built. The first model was the logistic regression. A logistic regression estimates the probability of a binary categorical dependent variable to be “1” (Eyduran, 2005; Hosmer et al., 2013; Altay et al., 2019). The mathematical model of the logistic regression (logit model) is described in equation 7:

(7) $\text{P}\left(\text{Y}=1|{\text{X}}_{\text{i}}\right)=\frac{{\text{e}}^{{\text{β}}_0+{\text{β}}_1{\text{X}}_1+{\text{β}}_2{\text{X}}_2+\dots +{\text{β}}_{\text{p}}{\text{X}}_{\text{p}}}}{1+{\text{e}}^{{\text{β}}_0+{\text{β}}_1{\text{X}}_1+{\text{β}}_2{\text{X}}_2+\dots +{\text{β}}_{\text{p}}{\text{X}}_{\text{p}}}}$

where: X is the independent variable probability of F. hepatica, when it receives the x value; P(Y=1) is the probability of the occurrence of Y = 1, when X = x; β₀ is the constant of the regression; and e is the natural logarithm (approximately 2.718).

The effects of the independent variables on the binary dependent variable can be explained by the logistic regression analysis using this model.

After setting up the logistic model, the first algorithm implemented was the k-nearest neighbor (KNN). This algorithm is a supervised one, well-known for its simplicity. It can be used to predict a target value. Unlike traditional algorithms, it does not define a model, but it represents each observation as a standard Euclidean distance within an n-dimensional space (instance). When new instances are considered, the KNN algorithm calculates the distance belonging to each training instance (Mitchel, 1997; Uğuz, 2019). In the KNN algorithm, K is the only parameter that determines how many neighbors will be evaluated, to decide the classification of a new observation.

The second implemented algorithm was the classification and regression tree (CART); developed by Breiman et al. (1984), CART is a tree-based algorithm, and it is not presented in a mathematical form. One of the most significant advantages of the CART algorithm is that it does not require assumptions of normality, homogeneity of variances, and independency of observations, which are assumptions for multiple regression. The second important advantage is that during the tree construction stage, statistically insignificant independent variables are excluded from the tree diagram (Kayri & Boysan, 2008; Coşkun et al., 2023).

The third algorithm used was the random forest (RF). The RF algorithm creates different decision trees by subsampling different observations in the dataset. This prevents overfitting and provides a greater accuracy than a single decision tree such as CART (Breiman, 2001). The RF is a robust algorithm for overfitting, in comparison with other machine learning algorithms. It enables to working with as many independent decision trees as desired and it is quite fast because it does not perform any pruning (Breiman & Cuttler, 2005). In the present study, the criteria used to choose the branch in each node is the Gini index. The Gini index measures the homogeneity (“purity”) of randomly selected variables that form the best branches among all variables, that is, the probability of misclassification. (Akar & Gungor, 2012; Daniya et al., 2020; Tangirala, 2020). The Gini index is calculated using equation (8).

(8) $\text{Gini index}\left(\text{L}\right)=1-{\displaystyle {\sum }_{\text{i}=1}^{\text{j}}{\text{p}}_{\text{i}}^2}$

where: L states a dataset containing j different classes (“0” and “1” for the present study); express the relative frequency or the probability of an object being classified into a particular class.

When the Gini index decreases, the homogeneity of the class increases. For a branch to be selected as the best one, the Gini index of a child node should be lower than that of its parent node. To terminate the tree, the Gini index should reach zero, and branching stops when each child node contains only one class (“0” or “1”).

After setting the LR and the algorithms, five metrics were chosen to compare them. The performance metrics used for classification were the confusion matrix, accuracy, sensitivity, precision, and F1 score (Dişçi, 2012). The confusion matrix illustrates how many samples are in the right or wrong classes. The samples were classified as true positive (TP) or true negative (TN), when the number of samples are successfully classified as “1” (presence of F. hepatica) or “0” (absence of F. hepatica), respectively; and false positive (FP) or false negative (FN), when the samples were unsuccessfully classified as “1” or “0”, respectively.

Accuracy is a percentage of correctly classified individuals within the total predictions made by the machine learning algorithm. Equation (9) represents the accuracy formula.

(9) $\text{Accuracy}=\text{TP}+\text{TN}/\text{TP}+\text{TN}+\text{FP}+\text{FN}$

Sensitivity indicates the percentage of correctly predicted animals using the machine learning algorithm in all true positive cases. The formula for sensitivity is given in equation (10).

(10) $\text{Sensitivity}=\text{TP}/\text{TP}+\text{EN}$

Precision measures the performance of classification algorithms as the proportion oftrue positive predictions among all cases predicted as positive. Equation (11) presents the formula for precision.

(11) $\text{Precision}=\text{TP}/\text{TP}+\text{FP}$

The F1 score measures the accuracy of the test. It is useful for unbalanced datasets (Vujovic, 2021); its calculation is presented in equation (12).

(12) $\text{F}1\text{score}=\text{2}\times \text{TP}/\text{2}\times \text{TP}+\text{FN}+\text{FP}$

All analyses were performed using the R programing language (version 4.3.1). The caret package (version 6.0.94), which consists of several machine learning functions, was used to build predictive models (Kuhn, 2008). The glm, knn, rpart, and rfparameters were used for building logistic regression, k-nearest neighbor, CART, and random forest, respectively. The variable importance of the algorithms was visualized using the varImp function. The variable importance considers certain coefficients to determine the relationship between the dependent and independent variables. In linear regression algorithms, each independent variable is ranked according to correlation coefficients, to detect which independent variable is more important for the prediction of the dependent variable. Hence, it serves for the dimensionality reduction and for the feature selection that improve the predictive performance of the model. Selecting the most important independent variables that explain a large portion of the variance of the dependent variable can be crucial for identifying and building high predictive performance models.

Results and Discussion

In the training set subjected to the z-score outlier detection (Table 3), the accuracy scores of LR, KNN, CART, and RF were calculated as 0.61, 0.66, 0.62, and 0.63, respectively. The KNN algorithm achieved the highest accuracy (66%). The accuracy scores of LR, KNN, CART, and RF were slightly decreased in the testing set and were calculated as 0.61, 0.57, 0.57, and 0.58, respectively. In the testing phase, the LR model showed the best results for accuracy (0.61). Considering the TP values, LR, KNN, CART, and RF classified 1098, 1046, 1299, and 1287 samples, respectively. The number of FNs in the LR, KNN, CART, and RF were 496, 548, 295, and 307, respectively. In machine learning algorithms, the sensitivity (S) values ranged between 0.65 and 0.81 in the testing set. The CART and RF algorithms showed higher sensitivity, indicating that these algorithms were successful in correctly detecting fasciolasis in cattle. The F1 scores (F1) of the testing set for LR, KNN, CART, and RF were 0.56, 0.52, 0.58, and 0.58, respectively. Except for the KNN, the F1 scores are quite similar. Models with high precision values also yield relatively high F1 scores.

Table 3
Results of accuracy (AC), confusion matrix (CM), sensitivity (S), precision (P), and F1 score (F1) of the training and testing sets subjected to z-score outlier detection of the logistic regression (LR) and the algorithms k-nearest neighbor (KNN), classification and regression tree (CART), and random forest (RF).

Algorithms	Training set						Testing set
	AC	CM		S	P	F1	AC	CM		S	P	F1
LR	0.61	5,976	3,931	0.66	0.60	0.63	0.61	1,098	1,226	0.68	0.47	0.56
		3,060	5,105					496	1,677
KNN	0.66	6,476	3,553	0.71	0.64	0.68	0.57	1,046	1,365	0.65	0.43	0.52
		2,560	5,483					548	1,583
CART (DT)	0.62	7,281	5,104	0.80	0.58	0.68	0.57	1,299	1,605	0.81	0.44	0.58
		1,755	3,932					295	1,298
RF	0.63	7,218	4,915	0.80	0.60	0.68	0.58	1,287	1,560	0.81	0.45	0.58
		1,818	4,121					307	1,343

The performance metrics of the training and testing sets subjected to IQR outlier detection are presented (Table 4). In the training set, the accuracy score of LR, KNN, CART, and RF algorithms were 0.62, 0.65, 0.62, and 0.63, respectively. The accuracy results were almost the same. A similar pattern of scores was observed for sensitivity and precision. The sensitivity scores were 0.66, 0.73, 0.80, and 0.80 for LR, KNN, CART, and RF, respectively. The precision scores were 0.60, 0.63, 0.59, and 0.59, respectively. In the testing set, there was no remarkable differences for the outlier detection method used. In the testing set, the F1 scores were 0.55, 0.53, 0.57, and 0.57 for LR, KNN, CART, and RF, respectively. These results suggest that there were no significant differences between LR and the algorithms.

Table 4
Results of the training and testing datasets by IQR outlier detection.

Algorithm(1)	Train dataset(2)						Test dataset(2)
	AC	CM		S	P	F1	AC	CM		S	P	F1
LR	0.62	5,952	3,870	0.66	0.60	0.64	0.61	1,068	1,230	0.67	0.46	0.55
		3,046	5,128					519	1,663
KNN	0.65	6,579	3,893	0.73	0.63	0.68	0.57	1,088	1,441	0.68	0.43	0.53
		2,419	5,105					499	1,452
CART (DT)	0.62	7,206	5,009	0.80	0.59	0.68	0.57	1,273	1,594	0.80	0.44	0.57
		1,792	3,989					314	1,299
RF	0.63	7,198	4,903	0.80	0.59	0.68	0.58	1,265	1,566	0.80	0.45	0.57
		1,800	4,095					322	1,327

• ⁽¹⁾LR, logistic regression; KNN, k-nearest neighbor; CART (DT), classification and regression tree; RF, random forest. ⁽²⁾AC, accuracy; CM, confusion matrix; S, sensitivity; P, precision; F1, F1 score.

These results also suggest that LR has the highest performance, in comparison with any machine learning algorithms performed in the present study. In the literature, there is no study on the prediction or classification of Fasciola hepática in cattle, using machine learning algorithms. The RF and CART algorithms are tree-based algorithms; therefore, remarkably similar performances and variable importance of the RF and CART are acceptable.

Conclusions

1. According to the bovine carcass dataset from Uruguay, the LR algorithm slightly outperforms for accuracy, sensitivity, precision, and F1 score.

2. For outlier detection, the IQR and z-score techniques give quite similar results and do not show any remarkable effects to improve classification metrics for this dataset.

Acknowledgements

To Luis Corbellini, Ricardo Almeida da Costa, Franklin Riet-Correa, and Eleonor Castro-Janer, for sharing their data on Mendeley Data, making it open and accessible.

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Publication Dates

• Publication in this collection
  02 Sept 2024
• Date of issue
  2024

History

• Received
  01 Nov 2023
• Accepted
  29 May 2024