Sateesh Kumar Pradham

Bio: Sateesh Kumar Pradham is an academic researcher. The author has contributed to research in topics: Association rule learning & Cluster analysis. The author has an hindex of 1, co-authored 1 publications receiving 38 citations.

A Survey of Data Mining Techniques on Medical Data for Finding Locally Frequent Diseases

Abstract: In the last decade there has been increasing usage of data mining techniques on medical data for discovering useful trends or patterns that are used in diagnosis and decision making. Data mining techniques such as clustering, classification, regression, association rule mining, CART (Classification and Regression Tree) are widely used in healthcare domain. Data mining algorithms, when appropriately used, are capable of improving the quality of prediction, diagnosis and disease classification. The main focus of this paper is to analyze data mining techniques required for medical data mining especially to discover locally frequent diseases such as heart ailments, lung cancer, breast cancer and so on. We evaluate the data mining techniques for finding locally frequent patterns in terms of cost, performance, speed and accuracy. We also compare data mining techniques with conventional methods.

Analysis of data mining techniques for heart disease prediction

Abstract: Heart disease is considered as one of the major causes of death throughout the world. It cannot be easily predicted by the medical practitioners as it is a difficult task which demands expertise and higher knowledge for prediction. This paper addresses the issue of prediction of heart disease according to input attributes on the basis of data mining techniques. We have investigated the heart disease prediction using KStar, J48, SMO, Bayes Net and Multilayer Perceptron through Weka software. The performance of these data mining techniques is measured by combining the results of predictive accuracy, ROC curve and AUC value using a standard data set as well as a collected data set. Based on performance factor SMO and Bayes Net techniques show optimum performances than the performances of KStar, Multilayer Perceptron and J48 techniques.

Dietary prediction for patients with Chronic Kidney Disease (CKD) by considering blood potassium level using machine learning algorithms

Abstract: Kidney damage and diminished function that lasts longer than three months is known as Chronic Kidney Disease (CKD) The primary goal of this research study is to identify the suitable diet plan for a CKD patient by applying the classification algorithms on the test result obtained from patients' medical records The aim of this work is to control the disease using the suitable diet plan and to identify that suitable diet plan using classification algorithms The suggested work pacts with the recommendation of various diet plans by using predicted potassium zone for CKD patients according to their blood potassium level The experiment is performed on different algorithms like Multiclass Decision Jungle, Multiclass Decision Forest, Multiclass Neural Network and Multiclass Logistic Regression The experimental results show that Multiclass Decision Forest algorithm gives a better result than the other classification algorithms and produces 9917% accuracy

Data mining techniques for medical data: A review

Abstract: Data mining is an important area of research and is pragmatically used in different domains like finance, clinical research, education, healthcare etc. Further, the scope of data mining have thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare which is an active interdisciplinary area of research. In fact, the task of knowledge extraction from the medical data is a challenging endeavor and it is a complex task. The main motive of this review paper is to give a review of data mining in the purview of healthcare. Moreover, intertwining and interrelation of previous researches have been presented in a novel manner. Furthermore, merits and demerits of frequently used data mining techniques in the domain of health care and medical data have been compared. The use of different data mining tasks in health care is also discussed. An analytical approach regarding the uniqueness of medical data in health care is also presented.

Machine Learning Techniques for Thyroid Disease Diagnosis: A Systematic Review

Abstract: In Disease Diagnosis recognition of patterns is so important for identifying the disease accurately. Machine learning is the field which is used for building the models that can predict the output based upon the inputs which are correlated based upon the previous data. Disease identification is the most crucial task for treating any disease. Classification algorithms are used for classifying the disease. There are several classification algorithms and dimensionality reduction algorithms used. Machine Learning gives the PCs the capacity to learn without being modified externally. By using the Classification Algorithm a hypothesis can be selected from the set of alternatives the best fits a set of observations. Machine Learning is used for the high-dimensional and the multi-dimensional data. Classy and automatic algorithms can be developed using Machine Learning.