Data Mining for Animal Health to Improve Human Quality of Life:

Insights from a University Veterinary Hospital

Oscar Tamburis

, Elio Masciari

, Christian Esposito

and Gerardo Fatone

Dept. of Veterinary Medicine and Animal Productions, Federico II University, Via Delpino 1, Naples, Italy

Dept. of Computer Science and Electrical Engineering, Federico II University, Naples, Italy

Dept. of Computer Science, University of Salerno, Fisciano (SA), Italy

Keywords: Veterinary Medicine, Electronic Medical Record, Decision Tree Algorithm, One Health.

Abstract: The increasing importance of Veterinary Informatics is driving the implementation of integrated veterinary

information management systems (VIMS) for the capture, storage, analysis and retrieval of animal data. In

this paper, a decision tree algorithm was implemented, starting from the database of the University Veterinary

Hospital at Federico II University of Naples, aiming at building a predictive model for an effective recognition

of neoplastic diseases and zoonoses for cats and dogs focusing to Campania Region, in order to figure out,

according to the One (Digital) Health perspective specifics, the connection between humans, animals, and

surrounding environment.

1 INTRODUCTION

Animals, be they categorized as pets, livestock, or

wildlife, stand as essential element in the evolution of

human race for countless reasons. In particular,

animal healthcare–related aspects play a prominent

role because of their strict connections with human

health. The monitoring of both wildlife and syntropic

species’ health state can provide in fact valuable

information about (i) the quality of the environment

they live in, and that they share with humans, in terms

of pollution level, as well as food safety and

traceability management; (ii) the occurring of

zoonotic phenomena (for instance, leptospirosis and

the recent COVID-19 pandemic). Furthermore, many

non-infectious diseases (e.g. diabetes, cancer, and

renal failure) are similar in both animals and humans

(Smith-Akin et al., 2007). As a consequence, the need

for an effective tracking of veterinary information to

facilitate integration of animal medical data to

support Public Health, has become essential. As a

matter of fact, under the epidemiological perspective

the advantages of using animals as sentinels or

comparative models of human diseases are well

https://orcid.org/ 0000-0002-0130-7915

https://orcid.org/ 0000-0002-1778-5321

https://orcid.org/ 0000-0002-0085-0748

https://orcid.org/ 0000-0003-2578-7420

known, as animals – or better, animal sentinels – may

be sensitive indicators of environmental hazards and

provide an early warning system for public health

interventions (Vilhena et al., 2020). With specific

reference to Campania Region, this kind of studies are

of particular concern due to the widely known so-

called “Terra dei Fuochi/Land of Fires” phenomenon

(see e.g. Zaccaroni et al., 2014; Cavallo et al., 2018).

An as important aspect relates then to the control of

the zoonoses, i.e. those diseases that can be

transmitted from the animals to the human beings via

faeces, urine, saliva, or blood. It is the case of e.g.

intestinal parasites and ticks (that use the animal as a

vector), or rabies (transmitted via the saliva). Such

risks have to be carefully taken into account when it

comes to the cohabitation between humans and

(conventional as well as non-conventional) pets

(Salyer et al., 2017; Mhlanga, 2020). To this end, it

becomes useful to resort to data mining computational

methods for extracting knowledge also in the case of

animal large databases deployed in integrated

veterinary information management systems (VIMS)

(Plavšić et al., 2009). Among the most diffused data

mining algorithms (Masciari, 2012; Ficco et al., 2015;

Tamburis, O., Masciari, E., Esposito, C. and Fatone, G.

Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital.

DOI: 10.5220/0010517201570164

In Proceedings of the 10th Inter national Conference on Data Science, Technology and Applications (DATA 2021), pages 157-164

ISBN: 978-989-758-521-0

157

Ianni et al., 2020), decision tree provides a tree-based

classification for developing a predictive model

according to independent variables (Bernardi et al.,

2017; Haq et al., 2020).

In this paper the main results will be shown from

the analysis of the data extracted from PONGO

software ©, i.e. the first EMR solution implemented

in the University Veterinary Teaching Hospital (it.:

OVUD, acronym for Ospedale Veterinario

Universitario Didattico) of the “Federico II”

University of Naples, Italy. The main goal was to

establish, by means of decision tree algorithm, a

predictive model for an effective recognition of

neoplastic diseases and zoonoses using clinical data,

according to clinical, para-clinical, and demographic

attributes. The investigation on the quality of clinical

data of OVUD’s patients is intended for helping, at

least on a region-wide scenario, to find out the

presence of specific connections between people’s

health, animal health, and their surrounding

environment, thus conveying the specific Public

Health dimension into the greater One (Digital)

Health scenario (Gamache et al., 2018; Magnuson &

Dixon, 2020; Benis et al., 2021).

2 MATERIALS AND METHODS

2.1 Subjects

The data extracted from PONGO sw in form of MS

Access DB relate to the general physical examination

(GPE), that is the first visit performed from the

veterinarian when the animal arrives to the hospital.

The database contains about 10360 rows (one row per

animal access) which span over a period going from

2010 to mid-2020. The visits were mainly performed

on pets, i.e. dogs (n = 8925; 86%) and cats (n = 1181;

11%). Horses occurred to be treated in the hospital as

well (n = 160; 2%). Only for a small part (n = 92; 1%)

the animals examined belonged to other species

(ducks, donkeys, bovines, buffaloes, goats,

lagomorphs, rodents, tortoises, and birds). Besides

animal species and date of the visit, the main fields of

the DB also related to age and sex of the animal, main

health issue (HI) acknowledged during the GPE, type

of feeding (e.g. commercial vs. homemade), and

vaccination status information. Also considered in the

study were the kind of environments the animal used

to live in (e.g. in an apartment, or outdoors), and the

Italian province it came from. As for the latter point,

the research was limited to the provinces of Campania

Region, due to the marginal number of rows related to

patients coming from other Italian regions. Table 1

describes the accesses to OVUD, on the basis of the

geographic provenance, for dogs, cats, and horses. A

number of OLAP operations (Pešić et al., 2009; Lu &

Keech, 2015) were performed to investigate the quality

of clinical data of OVUD’s patients for the considered

time period. Given the situation, it was decided to focus

the investigation only on dogs and cats.

Table 1: Distribution of dogs, cats, and horses that accessed

the OVUD, according to the Italian provinces.

Species Province # %

Dog

Avellino 145 2%

Benevento 71 1%

Caserta 753 8%

Napoli 6967 78%

Salerno 444 5%

Othe

Italian

rovinces 545 6%

Cat

Avellino 22 2%

Benevento 9 1%

Caserta 68 6%

Napoli 975 83%

Salerno 41 3%

Othe

Italian

rovinces 66 6%

Horse

Avellino 3 2%

Benevento 7 4%

Caserta 14 9%

Napoli 64 40%

Salerno 49 31%

2.2 Accesses per Animal Sex

Four types of sex specifications have to be considered

for animals: male (M), castrated male (MC), female

(F), spayed female (FS). Figure 2 reports the accesses

to the OVUD of dogs and cats, respectively, for the

time period considered. The number of rows/visits for

which it was not possible to retrieve the sex of the

animal, were also reported. Only in one case, the

animal (dog) was reported as not visited after the

access in the hospital. The lower number of accesses

registered in 2016 in both cases, was due to a partial

stop of the OVUD activities, as a structural collapse

interested at the end of 2015 part of the University

Department that hosts the hospital itself. The number

of male dogs’ accesses is about twice as much the

female accesses in almost all the years considered, with

quite lower numbers for the neutered dogs. A different

situation concerns cats, where the differences M/MC

and F/FS tend to be proportionally shorter, sometimes

in favour of the neutered exemplars.

2.3 Health Issues per Year

It was possible to identify about 140 different

diagnoses from the GPE for the period considered.

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Figure 1: Accesses to OVUD of dogs (left) and cats (right).

For mere space reasons, it was decided for both dogs

and cats to investigate, for each year, only the three

most relevant health issues (HIs), as reported in Tables

2 and 3. In case of HIs featuring the same occurrences,

they were all considered. The only exception is for

cats’ HIs in 2012, where the occurrences for HI #3

were equal to 1 for a very large set of issues, so it was

decided not to report them in the table.

Table 2: The three most diagnosed health issues for dogs.

Year HI #1 HI #2 HI #3

2010 Limping

Injury of

abdomen

Firm lymph node

(on exam.)

2011 Limping

On exam. -

inspection of

vomit

Pain in eye; Skin

lesion (on exam.);

Cough

2012 Alopecia

Skin lesion (on

exam.

)

;Lim

On exam. -

ins

ection of vomit

2013 Limping Alopecia

On exam. -

ins

ection of vomit

2014 Limping

Neoplastic

disease

Alopecia

2015 Limping

Neoplastic

disease

Alopecia

2016 Limping

Injury of

abdomen;

Alopecia

Firm lymph node

(on exam.)

2017 Limping

Firm lymph node

(on exam.);

Alopecia

Neoplastic disease

2018 Limping

Injury of

abdomen

Neoplastic disease

2019

Injury of

abdomen

Cough;

Neoplastic

disease

Limping

2020

Injury of

abdomen

Alopecia

On exam. -

inspection of vomit

Table 3: The three most diagnosed health issues for cats.

Year HI #1 HI #2 HI #3

2010

On exam. -

inspection of

vomit; Pain in

eye

Injury of

abdomen

Urinary tract pain

2011

On exam. -

inspection of

vomi

Alopecia Urinary tract pain

2012

On exam. -

inspection of

vomit; Pain in

eye

Firm lymph node

(on exam.);

Alopecia

2013

On exam. -

inspection of

vomi

Pain in eye;

Injury of

abdomen

Alopecia

2014 Pain in eye

Injury of

abdomen

Urinary tract pain

2015

Alopecia; On

exam. -

inspection of

vomit

Neoplastic

disease

Skin lesion (on

exam.); Pain in

eye

2016

On exam. -

inspection of

vomi

Injury of

abdomen

Alopecia

2017

Injury of

abdomen

Skin lesion (on

exam.)

On exam. -

inspection of

vomit; Urinary

tract pain;

Neoplastic

disease

2018

On exam. -

inspection of

vomi

Alopecia; Pain in

eye; Injury of

abdome

Skin lesion (on

exam.)

2019

Injury of

abdomen

Cough

Pain in eye;

Alopecia

2020

Injury of

abdomen

Firm lymph node

(on exam.)

Skin lesion (on

exam.); Closed

fracture of hip;

Sore mout

Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital

159

It can be noticed for dogs a diffuse presence of

limping–related issues (N = 458), along with

Neoplastic diseases (N = 263), and alopecia (N =

239). Injury of abdomen (N = 46), inspection of vomit

(N = 46), and pain in eye (N = 40) appear instead

among the most diffused issues reported for the cats

that accessed the OVUD. The occurrences of such

HIs during the years are reported in Figure 2. The

total number of occurrences are depicted in Figure 3.

In both cases, it is worth noticing the presence of

neoplastic diseases (dogs: N = 263; cats; N = 9) and

firm lymph node–related (dogs: N = 95; cats; N= 3)

diagnoses. Moreover, considering animals’ age of

birth (spanning from 1984 to 2020), it was possible to

compare for each trimester the diagnoses of firm

lymph nodes and neoplastic diseases. This revealed

that the 44% cases of dogs of the same age, and the

5% cases of cats of the same age presented a number

of occurrences of firm lymph node–related diagnoses

greater or at least equal to neoplastic diseases

diagnoses, thus inducing – at least for dogs – the

reasonable hypothesis of an existing connection

between the two pathologies. Furthermore, Figure 5

reports the occurrences of those diagnoses which can

be somehow related to the transmission of zoonoses,

from tetanus (N = 1 for dogs) to vomit (dogs: N = 254;

cats: N = 53). The number of occurrences of such

diagnoses is the 7% of the total occurrences registered

in OVUD for dogs and cats for the period considered.

2.4 Dataset

A preliminary step of dataset cleansing was necessary,

especially for what concerns the health diagnoses, as

no form of clinical standardized terminology had been

deployed. Moreover, for about 30% rows (N = 3729),

such type of data was actually missing, and only in a

limited number of cases it was possible to get to it

anyway by means of the analysis of the remainder

fields of the database. Eventually, the total number of

participants considered in the model were 10108.

Given the mentioned importance of identifying the

presence of neoplastic diseases–related and/or

zoonoses–related diagnoses, the need emerged to

figure out a way to predict the presence of symptoms

for both the issues considered – for both dogs and cats,

who also happen to live very close to humans. In

particular, according to what depicted in Figure 4, for

what concerns zoonoses it was decided to consider for

the analysis the diagnosis of “inspection of vomit”.

Figure 2: Distribution of the three most diagnosed health issues per year, for dogs (up) and cats (down).

DATA 2021 - 10th International Conference on Data Science, Technology and Applications

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Figure 3: Total occurrences for the three most diagnosed health issues, for dogs (up) and cats (down).

2.5 DT ID3 Feature Selection

Algorithm

The implementation of a Decision Tree Algorithm

(DT) appeared as the most suitable way to investigate

the membership of the subjects to different categories

(diagnosed with neoplastic disease, or not; diagnosed

with vomit, or not), taking into account the values of

specific attributes (predictor variables), which in our

case were identified for both cases as: animal sex,

diet, vaccination, feeding routines, and living

environment (plus the eventual presence of diagnosis

of firm lymph nodes, for neoplastic diseases). In order

to achieve these goals, a filter-based strategy using

DT ID3 (Iterative Dichotomiser 3) was proposed

(Gharehchopogh et al., 2012; Tayefi et al., 2017).

As it is common in data mining methods to divide

the dataset into two parts, also in our case the original

sample was split into a training set (to train the

model), and a test set (to evaluate the performance of

DT ID3). In particular, the original Training dataset

for the DT (oTrDS) featured all the accesses of dogs

and cats to the OVUD between 2010 and 2018 (N =

8643; 86%), while the original Testing dataset

(oTeDS) comprised the remaining accesses between

2019 and 2020 (N = 1465; 14%). The reason why it

was not respected the common rule according to

which oTrDS ≃ 70% sampling data, and oTeDS ≃

remaining 30%, mainly depends on two factors: (i)

the reduced accesses to OVUD in 2016 due to the

mentioned structure collapse, and; (ii) available data

from year 2020 only cover the first six months. Since

the aim of the study was to make prediction for two

kind of health issues, each per two animal species,

four specific Training datasets (sTrDS) and four

specific Testing datasets (sTeDS) were extracted

from oTrDS and oTeDS, respectively. For each case,

a confusion matrix was used to evaluate the

performance of the DT for classification of

participants. Accuracy, sensitivity, and specificity

were then measured for comparison. For sake of

simplification, decision tree and confusion matrix

have been represented in the following for one case

only (presence of symptoms for neoplastic disease in

dogs). A comparison was instead conducted for the

performances of all four algorithms.

3 RESULTS

A decision tree was built starting from the sTrDS

related to the recognition of neoplastic disease for

dogs (N = 8927). The sTeDS (N = 1305) was used to

evaluate the model. The input variables were animal

sex, diet, vaccination, feeding routines, living

environment, and eventual presence of diagnosis of

firm lymph nodes. As seen, since for dogs the

possibility of a correlation was recognized between

the diagnoses of neoplastic disease and firm lymph

nodes, the number of subjects positive for both health

issues (ND+ and L+) was reported in the algorithm.

ID3 uses two metrics to measure the importance of

the input variables, or features, such as entropy (the

measure of the amount of uncertainty) and

information gain (the difference between the entropy

of the DS, and the one related to the single feature).

So, be DS a given dataset, and X the set of variables

in DS. For each x ∈ X, the less the entropy, the more

the information gain. For each iteration, the algorithm

selects the feature with the smallest entropy/largest

information gain value. The final decision tree with

size 15, 8 leaves and 5 layers is shown in Figure 5.

Data Mining for Animal Health to Improve Human Quality of Life: Insights from a University Veterinary Hospital

161

Figure 4: Total occurrences of zoonosis–related diagnoses, for both dogs and cats.

Figure 5: Decision Tree to evaluate the presence of symptoms for neoplastic disease in dogs.

The evaluation of the tree was undertaken using

confusion matrix on a testing dataset, as shown in

Table 4. The algorithm had an Accuracy of 99%: of the

70 animals diagnosed as ND+ in the sTeDS, 60 were

correctly classified using the DT. In a subordinate

position, of the 50 animals diagnosed as L+, 37 were

correctly identified. The specificity and sensitivity of

the tree were equal to 99,2 and 1, respectively. The

performance of DT was also reported in Table 5.

Table 4: Confusion Matrix of sTeDS related to the

recognition of neoplastic disease for dogs.

Predicted outcome

ND+ ND-

Expected

outcome

ND+ 60 (TP) 10 (FP)

ND- 0 (FN) 1235 (TN)

Table 5: Performance of the DT ID3 model for the case

investigated.

Variable Decision Tree Model

Sensitivit

(

95% CI

)

(

93,9

–

)

Specificit

(95% CI) 99,2 (98,5

–

99,6)

Accurac

(95% CI) 99 (98,3

–

99,4)

An overall comparison was instead conducted

between the performances of the algorithm for the

four cases investigated, as reported in Table 6.

Although the numbers of cats-related diagnoses

extracted from the PONGO DB were significantly

lesser than the dogs-related ones, the overall results

obtained confirmed anyway the validity of the data

mining algorithm implemented, which turned as

highly capable of modelling the process of healthcare

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162

provision (Tamburis, 2019), as well as of setting forth

reliable measurements of system performance and

outcomes (Luzi et al., 2017).

Table 6: Comparison of the performances of the DT ID3

algorithm for all the cases investigated.

Accuracy Sensitivity Specificity

Dogs

Neoplastic

Disease

99% 100% 99,2%

Zoonosis

(Vomit)

100% 100% 100%

Cats

Neoplastic

Disease

100% 100% 100%

Zoonosis

(Vomit)

100% 100% 100%

4 DISCUSSION AND

CONCLUSIONS

In this paper a decision tree algorithm was

implemented, starting from the database of the

University Veterinary Teaching Hospital of the

Federico II University of Naples, to work out a

predictive model for an effective recognition of

neoplastic diseases and zoonoses using clinical data,

according to clinical, para-clinical, and demographic

attributes. The main scope was to investigate whether

and at what extent relations can stand between human

and animal health, and their surrounding

environments. The whole set of disciplines broadly

dealing with the such kind of “connecting chain” goes

under the name of One Health (OH), introduced for

the first time as part of the twelve “Manhattan

Principles” calling for an international,

interdisciplinary approach to prevent diseases

(Mackenzie & Jeggo, 2019) and specifically animal-

human transmissible and communicable ones. Seen

under this comprehensive point of view, the bursting

of dynamics connected to the emerging and re-

emerging of infectious diseases from national to

supranational contexts, as well as the need to identify

at global level risk factors and causes of health

problems that arise at the human-animal-environment

crossing, made even more remarkable the role of

veterinarians towards the protection of human health.

This points out therefore the growing of veterinary

informatics, as also encompassing the need for new

paradigms, approaches and technologies to reinforce

the capacity of traditional surveillance systems for

prevention and control of zoonoses, in terms of i.e.

inter-sectoral coordination, link between human and

animal health data and consequent management of

flows of reliable data and information, or proper use

of infrastructures, systems and human resources to

detect outbreaks (Choi et al., 2016).

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