Disease-Detection-Using-DNA-sequencing

As a result of the necessity of comprehending the activities of genome sequences at the time, DNA chips have been created throughout that process. DNA Large volumes of gene data have been generated by microarray technology, which also makes it simple to track the simultaneous expression patterns of thousands of genes in specific experimental settings. It was anticipated that using microarray technology would aid in the accurate prediction and detection of cancer. A crucial factor in the therapy of cancer is the precise classification of the disease. DNA arrays are made up of several DNA molecules that have been spotted in a specific pattern on a solid surface. When a DNA spot's diameter is less than 250 microns and greater than 300 microns, DNA arrays are referred to as macroarrays, respectively. Because at least hundreds of genes can be placed on the DNA microarray to be examined, it is so powerful that we can research the gene information quickly. DNA microarrays are made up of hundreds of unique DNA sequences that are printed in a high density array using a robotic arrayer on a glass microscope slide. creating a technique that leverages genetic microarray data to identify illness classes using machine learning techniques. This study intends to assess various machine learning methods for disease prediction. The dataset was taken from a DNA microarray, which simultaneously monitors the expression levels of many different genes. Patients are represented by samples in the datasets; 7070 gene expressions (values) for each patient are quantified in order to categorise their disease into one of the following cases: EPD, JPA, MED, MGL, or RHB. The features were selected using CHI2 method and mutual information. Subsequently, Then we employed Classifier algorithms and trained our data : KNN, Random Forest, Decision Tree, and MLP Naive Bayes and obtained the highest degree of accuracy using the Random Forest by the chi2 selection method and For mutual information Feature selector MLP gave best accuracy.