OJHAS: Vol. 5, Issue
3: (2006 Jul-Sep)
for Three Inborn Metabolic Disorders: A Pilot Study
Bioinformatics Division, Department of Botany, Bharathiar University,
Sarbadhikari SN, MBBS, PhD
TIFAC-CORE in Biomedical Technology, Amrita
Vishwa Vidyapeetham, Amritapuri 690525
Ananth N Rao, PhD
Metabolic Disorders Laboratory, Amrita Institute of Medical Sciences
and Research Centre, Elamakkara, Kochi - 682026
Address For Correspondence
Dr. S. N. Sarbadhikari, MBBS, PhD
TIFAC-CORE in Biomedical Technology,
Amritapuri 690525, India.
Kavitha S, Sarbadhikari SN, Rao AN. Automated Screening
for Three Inborn Metabolic Disorders: A Pilot Study.
Online J Health Allied Scs.2006;3:1
Submitted Aug 29, 2006; Revised Oct 1, 2006; Re-Revised: Nov 10, 2006; Accepted: Nov
15, 2006; Published: Dec 7, 2006
Inborn metabolic disorders (IMDs) form a large group of rare, but often
serious, metabolic disorders. Aims: Our objective was to construct
a decision tree, based on classification algorithm for the data on three
metabolic disorders, enabling us to take decisions on the screening
and clinical diagnosis of a patient. Settings and Design: A non-incremental
concept learning classification algorithm was applied to a set of patient
data and the procedure followed to obtain a decision on a patient’s
disorder. Materials and Methods: Initially a training set containing
13 cases was investigated for three inborn errors of metabolism.
Results: A total of thirty test cases were investigated for the
three inborn errors of metabolism. The program identified 10 cases with
galactosemia, another 10 cases with fructosemia and the remaining 10
with propionic acidemia. The program successfully identified all the
30 cases. Conclusions: This kind of decision support systems
can help the healthcare delivery personnel immensely for early screening
Decision support techniques, Metabolic diseases, Computer-assisted diagnosis,
Disorders (IMDs) (1) are inherited disorders that are caused
by a defect in a single gene. Some IMDs produce relatively unimportant
physical features or skeletal abnormalities. Others produce serious
disease and even death. Most inborn errors of metabolism are monitored
by routine blood or urine tests.
(1) is the inability of the body to use the simple sugar galactose
(causing the accumulation of galactose 1-phosphate), which then reaches
high levels in the body, causing damage to the liver, central nervous
system and various other body systems. Hereditary fructose intolerance (1)
is a metabolic disease caused by the absence of an enzyme 1-phosphofructaldolase. Propionic acidemia (1) is an inherited
disorder in which the body is unable to process certain proteins and
lipids properly. The gene defect for Propionic Acidemia is an
autosomal recessive genetic trait and is unknowingly passed down from generation
Many of the
inborn errors of metabolism can be treated (1) effectively. Treatment
depends on the enzyme defect itself and how readily the compounds involved
can be eliminated or replaced.
(2) are excellent tools for making decisions where a lot of complex
information needs to be taken into account. They also help us to form
an accurate, balanced picture of the risks and rewards that can result
from a particular choice.
tree is a representation of a decision procedure for determining the
class of a given instance. In many problems chance (or probability)
plays an important role. Decision analysis is the general name that
is given to techniques for analyzing problems containing risk/uncertainty/probabilities.
Decision trees are one specific decision analysis technique. A decision
tree takes as input an object or situation described by a set of properties,
and outputs a yes/no decision.(2)
information retrieval and database management systems has been advancing
very quickly over the past few decades, especially in healthcare applications. One such information
system was the Internet platform oriented program developed by Hofestadt
et al (3) that provided complex and specific knowledge about inborn
errors of metabolism. Another program developed by Kauert et al (4)
provided diagnosis and therapy for IEMs. This information system could
be helpful for scientists and physicians working in the field of inborn
metabolic diseases. PROLOG, another computer aided expert system developed
by Pince et al (5) was used to obtain uniform and fast phenotyping and
reporting of dyslipoproteinemia.
Metanet (6) is a neural network based computer program for diagnosis of inherited
metabolic diseases in children, using plasma or urinary amino acid results
as input wherein the expert system asks a question to the user regarding
the presence or absence of common clinical and/or biochemical abnormalities.
Using both the amino acid data and the answers to the questions, the MetaNet program provides a provisional diagnosis. The diagnostic output
indicates the degree of confidence of the program in the diagnosis.
of the present study was to construct a decision tree from the clinical data on
certain inborn errors of metabolism so that the predictions with the tree can be used for
early diagnosis and interventions.
To the best of
our knowledge, this is the
first report from India for screening IMDs by automated classifiers.
errors of metabolism handled in this study were Galactosemia, Fructosemia
and Propionic Acidemia. The real data of patients was obtained from
the hospital to which the authors are affiliated.
The cases had
been referred from the Pediatrics and Neurology departments of the hospital, and were confirmed by chemical analysis from serum/urine.
Moreover, the incidences of such disorders are not high and the clinical
features are rarely documented properly.
Tree For The Problem
tree is constructed by looking for regularities in data. A decision
tree is an arrangement of tests that prescribes an appropriate test
at every step in an analysis. More specifically, decision trees classify
instances by sorting them down the tree from the root node to some leaf
node, which provides the classification of the instance. Each node in
the tree specifies a test of some attribute of the instance,
and each branch descending from that node corresponds to one of the
possible values for this attribute. An instance is classified by starting
at the root node of the decision tree, testing the attribute specified
by this node, then moving down the tree branch corresponding to the
value of the attribute. This process is then repeated at the node on
this branch and so on until a leaf node is reached.
The basic classification
algorithm (7) was a non-incremental concept learning method that produced a hypothesis in the form of a decision tree. The
algorithm accepted a training set of positive and negative examples
of a concept. The decision tree and rules produced from applying the
algorithm attempted to give a generalization that could be used to classify
objects not in the original training set. The classification algorithm
could be summarized as follows.
- The inputs:
Features in a training set
- The outputs:
A decision tree
- Step 1:
If all elements in T are positive then create a ‘yes’ node and halt.
- Step 2: If
all elements in T are negative then create a ‘no’ node and halt.
- Step 3: Otherwise
select an attribute F with values V1, V2, V3,
... , Vn. Partition T into
subsets T1, T2, T3, ..., Tn
according to their values on F. Create a branch with F as parent and
T1 etc. as child nodes.
- Step 4: Apply
the procedure recursively to each child node.
The input to
this decision tree was a set of instances containing information about
the patients and their medical conditions. The output enabled us to
decide the type of inborn errors of metabolism in the patient. The input
data consisted of seven attributes namely seizures, mental Retardation, gastro intestinal
infection, respiratory tract
infection, liver dysfunction, hypoglycemia, and hepatomegaly with four
values each (-, +, ++, +++). The values -, +, ++, +++ indicate absent,
mild/low, moderate and severe respectively. Decision trees often used
in medical diagnosis structurally consist of two types of nodes: non
terminal (intermediate) and terminal (leaf). The former corresponds
to questions asked about the characteristic features of the diagnosed
case. These may be factorial, e.g., “Does the patient
have symptom X?”, or ordinal, e.g., “Is symptom X absent,
mild, moderate or severe?”, or continuous, e.g., “What is
the patient’s median motor latency at the wrist?”. (The above questions,
though not actually related to IEMs, are just examples to indicate the
mode of questioning in any diagnostic process.) Questions about the characteristic
features of the cases used in this study were ordinal i.e., questioning
whether the symptom is absent, mild, moderate or severe. The best value
was selected for the intermediate node. The terminal node, on the other
hand, generated decisions/diagnosis.
decision-making process, where a single question was asked each time
and, depending on the answer, a different branch of the tree containing
another set of questions was asked, achieved diagnosis. The root of
the tree contained the first diagnostic question asked by the classifier.
Depending on the answer for each particular case, a different branch
of the tree is traversed, arriving at a decision node. This method of
building decision trees was called top-down induction of decision trees
and was based on recursive partitioning of data. In brief, at each recursive
step, one question was asked which selects a best value. This question
partitioned the design set, and the process was repeated for each subsets,
until they reached a final decision. This was the method of applying
the classification algorithm for the patient’s real data to build
the decision tree classifier in order to understand the decision process.
training set containing 13 cases was investigated for the three inborn
errors of metabolism. Using a small set of training data for classification
is an essential step in any decision tree construction process. This
is because the initial classification of data using a training set
can give an idea about the categorization of the future instances.
Thirty cases of three types of inborn errors of metabolism were studied with
the programme and it could correctly identify the 10 cases with galactosemia,
another 10 cases with fructosemia and the remaining 10 with propionic
acidemia. There was no wrong identification of the normal cases as disordered
The results are summarized
in Table 1:
Performance of the Program for Screening Inborn Metabolic Disorders
tree programme identified the cases of inborn errors of metabolism correctly.
The decision tree pointed towards that particular provisional diagnosis
utilising a certain combination of all the seven (nonspecific) attributes
mentioned in the methodology. A clinician will not be able to “analytically
decide” all the permutation and combinations possible with 7 clinical features
(each having 4 grades of severity), whereas for a decision support system,
this is a rather simple, quick and easy job. Strengths of
the study therefore lies in the quick and correct identification of 3-5 close
differential diagnoses and laboratory investigations can be performed only in
such direction. Clinical symptoms in addition to output from the classifier
may be a quick way of probably initiating general management to be followed
by disorder specific management in all suspected cases of inborn errors
of metabolism. This shall avoid unnecessary expenditure on performing the tests
for the whole spectrum of disorders. As an output, the classifier shall also
help stratify which patient should receive expensive laboratory testing.
of the limitations of this study was the small sample size; 30 cases was too
small a number to test for the inborn errors of metabolism. But when more data
on inborn errors of metabolism is available, the decision tree can be expanded
and the validation of the program can be extended to many such cases. This was
only a pilot study and further study is in progress. Presently more samples
are being collected and other types of classifiers viz., Logistic Regression
Analysis, Artificial neural networks and Support Vector Machines (SVM)
are being tried out.
program in the present study differs from the other computer programs for the diagnosis of inherited metabolic diseases
in various ways. The
program developed for this study was a decision tree based computer
program which took as input the ordinal valued data of patient’s medical
conditions (whether the symptom is absent/low/moderate/severe) and the
output provided the provisional diagnosis. The present study program
differed from Metanet, a neural network program, in that it took
as input the amino acid results and the common clinical abnormalities
and the output provided the provisional diagnosis accompanied by a numerical
belief vector, which indicated the degree of confidence of the program
in the diagnosis. Information on diseases and their conditions, treatment
of such diseases and integration of that information with other databases
and the inclusion of statistical knowledge of such diseases were the
typical features of the other above described programs. Though the program
in the present study did not include such additional information, it
can be used as a tool for the diagnosis of IEMs.
investigators (8) had described sequential testing for clinical trials. The sequential
nature of the data was not from additional patients, but rather from
longer follow-up which we shall try to do.
neural networks had been used (9) for the parallel evaluation of all
fine-motoric data, leading to a reclassification of patients suffering
from Wilson’s disease, based on actual fine-motoric abilities but
not reflecting the clinical classification at the time of manifestation.
Presently we are not in a position to try such things at our Institute.
of machine learning applications to IMDs (10) endorse that the mined
data confirm the known and indicate some novel metabolic patterns that
may contribute to a better understanding of newborn metabolism. We have
been presently using some of their techniques but applying to our own
growing databases – which are very distinct from the Western population
systems that help physicians are becoming very important part of medical
decision making, especially in medical diagnostic processes and particularly
in those where decision must be made effectively and reliably.
The aim of
this work had been to study and apply a suitable technique for mining
medical data for prediction of or screening for the inborn metabolic
disorders namely galactosemia, fructosemia and propionic acidemia. This
approach was more suitable for the given set of minimal data on IMDs. The
ability to handle situations robustly and the ability to classify samples
made this approach attractive for healthcare applications. This kind
of decision support systems could help the healthcare delivery personnel
immensely for early screening of IMDs.
of the pilot study presented here, we are now trying various other data
mining methods on an extended database of clinical features of IMDs.
author thankfully acknowledges the financial support from TIFAC for
carrying out the research in TIFAC-CORE.
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