A Empirical study on Disease Diagnosis using Data Mining Techniques

Deepika, M.; Kalaiselvi, K.

doi:10.1109/icicct.2018.8473185

Cited by 24 publications

(4 citation statements)

References 5 publications

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“…Prediction of disease in starting stage is very effective to save the patient life and minimize the treatment cost. In data mining using various classification algorithms [11] for disease prediction and widely used data mining technique of Decision Tree Algorithm [12] which is used to disease prediction with the best accuracy of results.…”

Section: Save Lives Of Patients Using Predictive Medicinementioning

confidence: 99%

A Review in Prediction of Malnutrition Status Using Data Mining Techniques

Dhivya¹,

Sangeetha²

2022

APJHS

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Child nutritional deficiency comes under various facts such as malnutrition, low birth weight, infant and young child feeding, iodine, and Vitamin deficiency. In recent years, malnutrition is a widespread problem at a global level. This research produces a review of data mining techniques is used to predict the malnutrition status of young children. The root cause of child malnutrition varies across the regions in every country because of various impacts such as lifestyle, food intake, environmental changes, maternal care, and also motherhood care. Most of the research, the results predict the malnutrition status using some anthropometric parameters of preschool age from 5 to under10 age of children and clinical sign parameters are considered to predict the best accuracy. Recently, data mining uses the proposed method of clustering, classification techniques, Regression, and machine learning algorithms to predict the malnutrition status with the highest accuracy using anthropometric parameters (height, weight, and age) for stunted (low height-for-age) and wasted (low weight-for-height), underweight (low weight-for-age) and clinical sign attributes to predict the statuses of malnutrition with and without transformed attributes.

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Section: Save Lives Of Patients Using Predictive Medicinementioning

confidence: 99%

A Review in Prediction of Malnutrition Status Using Data Mining Techniques

Dhivya¹,

Sangeetha²

2022

APJHS

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“…Recently, the application of machine learning algorithms in medical science is grabbing the attention of researchers [4,5,7]. Many works have been done using different machine learning algorithms to diagnose and predict various diseases, and machine learning algorithms are accepted as the best for this purpose.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, machine learning algorithms are gaining a profound attention in medical science due to their high performance and effectiveness in predicting outcomes, decreased medicine expenses, high quality of healthcare, and their capability to make real-time decision in emergencies [4,5]. Many researchers have given efforts on BC diagnosis and prediction using various machine learning algorithms like Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), etc.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Management System Using Decision Tree and Neural Network

2021

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Nowadays, people are facing various health-related problems due to the modern life style what they follow. Breast Cancer is one of the most common problems among women worldwide which affects approximately 2.1 million women each year. Hence, it has become paramount to develop a system that can identify the major risk factors of Breast Cancer beforehand to make women aware about the risk factors and to take some precautionary measures to manage Breast Cancer. Consequently, this paper proposes a system called Transparent Breast Cancer Management System using P-Rules (TBCMS-PR) which identifies the major risk factors responsible for Breast Cancer in detail using decision tree and neural Network. TBCMS-PR uses decision tree to generate the rules for deciding the decision of Breast Cancer. Neural Network is used to keep only the relevant rules for Breast Cancer and to drop the irrelevant ones. Finally, the major risk factors with ranges are identified based on Sequential Search algorithm. The performance of the TBCMS-PR system is validated with the Breast Cancer dataset collected from UCI repository and is compared with a recent existing system. From the experimental results, it is observed that the proposed TBCMS-PR is significant and potential to manage Breast Cancer to a great extent by managing only one or two major risk factor(s).

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“…Decision tree [12][13] [14][15] [16] is utilized for classification in the decision-making process. It consists of two distinct nodes, the internal node and the leaf node.…”

Section: Decision Treementioning

confidence: 99%

Prediction of Liver Disease using Regression Tree

Vinutha¹,

Chandrika²

2021

Int. J. Onl. Eng.

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Abstract— Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important.The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not.

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A Empirical study on Disease Diagnosis using Data Mining Techniques

Cited by 24 publications

References 5 publications

A Review in Prediction of Malnutrition Status Using Data Mining Techniques

A Review in Prediction of Malnutrition Status Using Data Mining Techniques

Breast Cancer Management System Using Decision Tree and Neural Network

Prediction of Liver Disease using Regression Tree

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