Comparative Analysis of Classifiers for Prediction of Epileptic Seizures

Epilepsy is a neurological disease in which people suffer from seizure attack and lose the normal function of brain. Almost 50 million people have epilepsy in the world due to which it has become the most common neurological disease. Early prediction of epilepsy helps patients to avoid epilepsy and live normal life. Many studies have been conducted for the early prediction of epilepsy. However, selection of the most appropriate classifier has always been a question that needs to be resolved. In this study, we are using six classifiers of machine learning which are KNN, Naïve Bayes, Linear Classification Model, Discriminant Analysis Model, Support Vector Machine and Decision Tree, to find the best classifier for the prediction of epileptic seizures, in term of accuracy. Dataset from “Kaggle” was used. Preprocessing and cross-validation of the data was carried out for training and testing of classifiers. The results depict that Naive Bayes classifier has a better average accuracy of 95.739% as compared to other classifiers. The future work of this study is to implement the suggested model in real time, so that the workload of medical members could be reduced.

Raman Spectroscopy Based Diagnosis of Dengue Virus Infection in Human Blood Serum.

Dengue virus (DENV) infection is a mosquito born infectious disease. Its diagnostic is utmost important for treatment, as the symptoms of disease are quite similar to other diseases. Current pathological diagnostics methods available are reverse transcriptase polymerase chain reaction (RT-PCR) and enzyme linked immunosorbent assay (ELISA). RT-PCR is used to detect the virus itself while ELISA is used to detect non-structural protein-1 (NS1) and antibodies like immunoglobulin-M (IgM) and immunoglobulin-G (IgG). Existing methods e.g. virus isolation, RT-PCR and ELISA have certain disadvantages like more time consuming, false-positive/false-negative results and expensive as compared to Raman spectroscopy. Raman spectroscopic technique provides molecular signatures, minimum running cost and online results. Raman spectra of biological samples combined with a suitable statistical data-mining technique like partial least squares (PLS) regression can be used to devise a new method for diagnosis of DENV infection in human blood sera. In present studies, this technique is successfully applied for the diagnostic of DENV infection based on three steps. A graphical user interface (GUI) was specially designed and its code was developed in MATLAB (Mathworks 2009a) programming language to implement PLS for the presented research work. First step: Raman spectra of ELISA confirmed NS1 positive and negative sera samples are discriminated by PLS regression. Analysis of regression coefficients, which differentiate these groups, shows an increasing trend for phosphatidylinositol, ceramide and amide-III, and a decreasing trend for thiocyanate in the DENV infected serum. Second step: Raman spectra of samples, with known value of ELISA based AI of IgM are discriminated by PLS regression. Analysis of regression coefficients revealed that concentration of asparagine, glutamate, galactosamine etc. were found to increase while concentration of fructose, cholesterol, cellobiose, and arabinose were found to decrease with increasing values of antibody index (AI) of IgM. Third step: Raman spectra of samples, with known value of ELISA based AI of IgG are discriminated by PLS regression. Analysis of regression coefficients revealed that myristic acid, coenzyme-A, alanine etc. were found to increase, while amide III, collagen, proteins, fatty acids, phospholipids and fucose were found to decrease with increasing values of AI of IgG. Raman spectroscopy provides not only the diagnosis of DENV infection, but it also enables the detailed insight of the abnormalities appearing in molecular composition of a sample. Importantly, Raman spectra @ 532 nm excitation were used to investigate the possible use of lactate as biomarker for DENV infection. It was found that spectral difference in healthy and infected samples is due to an elevated level of lactate in DENV infected group.