Dengue infection belongs to the family of virus, Flaviviridae, consisting of four serotypes which spread through the chomp of contaminated Aedes mosquitoes. Around 2.5 billion individuals live in dengue-hazard locales with around 100 million new cases every year around the world. The worldwide predominance of dengue has grown dramatically in later decades. The illness is now endemic in more than 100 nations in Africa, the Americas, the eastern Mediterranean, South East Asia and the western pacific south Asia and the western pacific are the most genuinely influenced. In 1970's only nine nations had encountered DHF plagues, a number which had expanded more than four- crease by 1995[30].Numerous clinical signs are utilized for diagnosing of fever. In any case, it has been an awesome test for the doctors to distinguish the level of hazard in dengue patients utilizing clinical indications. But the disadvantages of clinical procedures make machine learning more powerful in diagnosing of fever in affected patients. Subsequently, this study plans to apply a non-invasive machine learning techniques to help the doctors for ordering the hazard in dengue patients. Conducted a comparison study among Simple Classification and Regression Tree(CART), Multi-layer perception (MLP) and C4.5 algorithms, based on which demonstrating that Simple CART algorithm shows 100% accuracy for classification of affected or unaffected patient.

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Malaria disease is one whose presence is rampant in semi urban and non-urban areas especially resource poor developing countries. It is quite evident from the datasets like malaria, dengue, etc., where there is always a possibility of having more negative patients (non-occurrence of the disease) compared to patients suffering from disease (positive cases). Developing a model based decision support system with such unbalanced datasets is a cause of concern and it is indeed necessary to have a model predicting the disease quite accurately. Classification of imbalanced malaria disease data become a crucial task in medical application domain because most of the conventional machine learning algorithms are showing very poor performance to classify whether a patient is affected by malaria disease or not. In imbalanced data, majority (unaffected) class samples are dominates the minority (affected) class samples leading to class imbalance. To overcome the nature of class imbalance problem, balancing the data samples is the best solution which produces the better accuracy in classification of minority samples. The aim of this research is to propose a comparative study on classifying the imbalanced malaria disease data using Naive Bayesian classifier in different environments like weka and using an R-language. We present here, clinical descriptive study on 165 patients of different age group people collected at medical wards of Narasaraopet from 2014-17. Synthetic Minority Oversampling Technique (SMOTE) technique has been used to balance the class distribution and then we performed a comparative study on the dataset using Naïve Bayesian algorithm in various platforms. Out of balanced class distribution data, 70% data was given to train the Naive Bayesian algorithm and the rest of the data was used for testing the model for both weka and R programming environments. Experimental results have indicated that, classification of malaria disease data in weka environment has highest accuracy of 88.5% than the Naive Bayesian algorithm accuracy of 87.5% using R programming language. The impact of vector borne disease is very high in medical applications. Prediction of disease like malaria is an hour of the need and this is possible only with a suitable model for a given dataset. Hence, we have developed a model with Naive Bayesian algorithm is used for current research.