A decision support system for multi-target disease diagnosis: A bioinformatics approach

Femi Emmanuel Ayo, Joseph Bamidele Awotunde, Roseline Oluwaseun Ogundokun, Sakinat Oluwabukonla Folorunso, Adebola Olayinka Adekunle

Abstract:

Malaria and typhoid fever are revered for their ability to individually or jointly cause high mortality rate. Both
malaria and typhoid fever have similar symptoms and are famous for their co-existence in the human body, hence,
causes problem of under-diagnosis when doctors tries to determine the exact disease out of the two diseases. This
paper proposes a Bioinformatics Based Decision Support System (BBDSS) for malaria, typhoid and malaria
typhoid diagnosis. The system is a hybrid of expert system and global alignment with constant penalty. The architecture
of the proposed system takes input diagnosis sequence and benchmark diagnosis sequences through the
browser, store these diagnosis sequences in the Knowledge base and set up the IF-THEN rules guiding the
diagnosis decisions for malaria, typhoid and malaria typhoid respectively. The matching engine component of the
system receives as input the input sequence and applies global alignment technique with constant penalty for the
matching between the input sequence and the three benchmark sequences in turns. The global alignment technique
with constant penalty applies its pre-defined process to generate optimal alignment and determine the
disease condition of the patient through alignment scores comparison for the three benchmark diagnosis sequences.
In order to evaluate the proposed system, ANOVA was used to compare the means of the three independent
groups (malaria, typhoid and malaria typhoid) to determine whether there is statistical evidence that the
associated values on the diagnosis variables means are significantly different. The ANOVA results indicated that
the mean of the values on diagnosis variables is significantly different for at least one of the disease status groups.
Similarly, multiple comparisons tests was further used to explicitly identify which means were different from one
another. The multiple comparisons results showed that there is a statistically significant difference in the values
on the diagnosis variables to diagnose the disease conditions between the groups of malaria and malaria typhoid.
Conversely, there were no differences between the groups of malaria and typhoid fever as well as between the
groups of typhoid fever and malaria typhoid. In order to show mean difference in the diagnosis scores between the
orthodox and the proposed diagnosis system, t-test statistics was used. The results of the t-test statistics indicates
that the mean values of diagnosis from the orthodox system differ from those of the proposed system. Finally, the
evaluation of the proposed diagnosis system is most efficient at providing diagnosis for malaria and malaria
typhoid at 97% accuracy.