A modern technology used for extracting knowledge from a huge amount of data using different models and tasks such as prediction and description is called data mining. The data mining approach has a great contribution on solving a different problem for data miners. This paper focuses on the application of data mining in health centers using different models. The model development process helps to identify or predict the behavior of blood donors whether they are eligible or ineligible to donate blood by their right status way and protects any blood bank health center from the collection of unsafe blood. Classification techniques are used for the analysis of Blood bank datasets in this study. For continuous blood donors, it will help to enable to donate voluntary individuals and organizations systematically. J48 decision tree, neural network as well as naïve Bays algorithms have been implemented in Weka to analyze the dataset of blood donors. The study is used to classify the blood donor's eligibility or ineligibility status based on their genders, deferral time, weight, age, body priced, tattoos, HIV AIDS, blood pressure, donation frequency, hepatitis, illegal drug use attributes. From the 11 attributes, gender does not affect the result. We have used 1502 datasets for the train set and 100 datasets for testing the model using cross-fold validation. Cross-fold data, partition was used in this study. The efficiency and effectiveness of the algorisms are measured automatically by the system. The obtained result showed that the J48 classifier outperforms the best result as well as both neural network and navies, Bayes, in terms of matrix evolution, with its 97.5% overall model accuracy has offered interesting rules.

Nowadays, various amount of information’s are available on the internet. To search relevant documents from the internet development of information retrieval system or search engines is necessary. Therefore, this paper deals with development of Information Retrieval system for Tigrigna textual documents. It helps to find relevant documents from the internet, which are stored in Tigrigna language for the Tigrigna language users to satisfy their information need. The system includes two sub systems those are indexing and searching part. The indexing part is the process of organizing filtered Tigrigna documents using keywords extracted from the entire Tigrigna collection or corpus. It is an offline process carried out by the producers or authors world to speed up searching of information from the entire document as per users query. Searching is the process of scanning documents to find relevant documents that matches to the users query or information need. It is an online process mostly carried out by the users or readers world. Vector space model techniques was applied to implement this system. Vector space model is the most core information retrieval technique used to calculate similarity measure between the query and the documents finally it ranks the most relevant documents to the given query according their similarity score in descending order. According to this, the retrieval system was tested and the experimental results of the system in Tigrinya documents returned an encouraging and promising result. The system has registered, 70% precision and 84% Recall.