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Infectious diseases like typhoides, dengues, etc. are caused by water and environmental changes. It is important to provide medications for such infection patients. Conventional approaches for the correct diagnosis of patients are not acceptable. To make an appropriate diagnosis it is important to accurately identify patient status. Although advancement in reliable diagnostics of technology remains an essential mission. This thesis suggested a classification approach for certain infectious diseases. 173 Typhoid fever patients and 119 Dengue Fever patients were collected clinically. Crafted a hybrid method using voting set technique that was constructed as base classification in machine learning techniques. The diagnostic results can be classified according to the classificator's majority of votes. Data set tests were carried out and the results were contrasted with traditional computer teaching methods, including the Group Baive Bayesian, K-Nearest Neighbor and the Decision Tree. Analysis indicates that, in the case of typhoid and dengue-fever infected patients, the suggested hybrid solution has a 97.2% precision. Focusing on infectious diseases, several traditional approaches were analysed and the proposed hybrid methodology focused on an ensemble voting strategy that showed the highest efficiency in classifying infectious diseases.

Leukemia – a type of blood cancer which was caused by abnormal growth of White Blood Cells (WBC) in bone marrow. Accurate identification of effected patients and diagnosing within time is most important otherwise it causes to death. Machine learning algorithms are most popular for accurate identification of patient status. Presenting 152 patients data which was clinically collected. In this paper, proposing Bagging with Random split function for classification of leukemia patients. Also compared the classifier performance with various ensemble techniques – Multiclass classifier, Logit Boost, Stacking and Random Committee classifiers. Experiments are conducted and proven that bagging with Random split function shows 95% accuracy on classification of leukemia disease dataset.

Image enhancement is a technique to give a better quality of an image in terms of its clarity, brightness, and to give the human eye comfortable to look at. There are different types of techniques to give good quality to an image. Global image contrast enhancement is one of the most commonly used techniques to enhance the quality of an image, but it has some disadvantages with the fact that it does not consider the local details of an image, that is the detailed texture of an image. In local contrast enhancement, it addresses the local details of an image and preserves the local details of the image. Local details of an image are very important while analyzing an image, which is that of the scientific study of an image like the image taken from planetary bodies, satellite image, and also in medical images. Local details of an image are very important for diagnosing a particular ailment. When we used either local contrast enhancement or global contrast enhancement alone, we faced the loss of brightness of the image. In order to address and reduce this discrepancy of individual enhancement methods, a new proposal that uses both these methods on the same image. First, the image is locally enhanced and the output is again processed by the global enhancement method, thereby giving a properly enhanced image without losing the brightness of the image. This enhancement method is simulated in MATLAB, and results are verified on the parameters of image.

Body area network (BAN) is a promising technology for monitoring different physiological parameters of the patients in real time. Particularly, when the BAN integrated with wireless technologies provides the complete medical infrastructure. Android is the most popular operating system in the smartphone. The wireless BAN combined with an Android-based smartphone offers a large functionality. Various medical parameters can be analyzed, stored, and visualized using the GUI of an Android application designed for the end user. The different sensors placed on the body of the patient acquire physiological data from patient. This acquired data is then, gone through signal processing and data analysis and results are sent to coordinator node. The physiological data are transferred, via bluetooth to an Android-based smartphone. The physiological parameters of the patients are continuously monitored by the system and if any variation occurred, it sends alert messages to the doctor. The alert is of two types, SMS alert and email alert. Using this alert system, the emergency situation can be handled effectively and the patient will get the medical care as soon as possible.