A novel approach for differential diagnosis of acute flaccid paralysis (afp) via data science

Abstract

Acute Flaccid Paralysis (AFP) is caused due to diseases like Poliomyelitis, Acute Transverse Myelitis (ATM), Guillain-Barre-Syndrome (GBS), Traumatic Neuritis (TrN), and others of similar nature. These diseases can damage various muscles or spinal cord, or peripheral nerves. Pakistan is among the few countries which are struggling to eradicate Poliomyelitis. The conventional diagnosis takes a lot of effort and time. A solution to this problem is provided in this research using the methods from Data Science. Objective: Diagnosis of AFP is done via two approaches, used by the framework proposed in this research: 1) Prediction of diagnosis for non-polio AFP cases, using patient’s dataset; 2) Prediction of clinical diagnosis for AFP, using the developed rule base data based on a standard chart, provided by the World Health Organization (WHO) for differential diagnosis of AFP. Method: 1) The laboratory diagnosis method predicts the diagnosis of non-polio AFP cases using patient data. The data was preprocessed and converted into a dataset; which was used for model training and prediction of diagnosis for non-polio AFP. 2) The clinical diagnosis method is based on the symptoms given in the standard table of WHO. This table differentiates between diseases like Poliomyelitis, GBS, TrN, TM, and Acute Childhood Hemiplegia. A rule base was devised according to this standard diagnosis table, with the five diseases acting as labels of the dataset. The rules helped in diagnosis for a particular AFP case. Decision Tree and Random Forest are the two algorithms used for model training and prediction in this research. The trained model predicts the disease for a particular AFP case. Results: For the first method, the prediction accuracy was 87.76% with the decision tree algorithm and 92.46% with random forest. For the second dataset, using the rule base dataset, the accuracy with the decision tree algorithm was 98.54% and 100% with random forest.