scholarly journals Data Mining Technique Based Critical Disease Prediction in Medical Field

2020 ◽  
Author(s):  
Preetha J ◽  
Raju S ◽  
Abhishek Kumar ◽  
Sayyad Samee ◽  
Vengatesan R
Keyword(s):  
Data Mining ◽  
Field Data ◽  
Focal Point ◽  
Heterogeneous Data ◽  
Medical Data ◽  
Disease Prediction ◽  
Medical Field ◽  
Data Mining Technique ◽  
Research Papers ◽  
Medical Dataset

In the present days’ deaths because of some critical disease has become a significant issue in the medical field. Data mining is one of the significant territories of research that is famous in wellbeing associations. Data mining has a functioning job for finding new patterns and examples in the healthcare association which is valuable for every one of the gatherings related to this field. The medical dataset has heterogeneous data as numbers, content, and pictures that can be mined to convey an assortment of helpful data for the physicians. The examples picked up from the medical data can be helpful for the physicians to find diseases, foresee the survivability of the patients after disease, the seriousness of diseases and so forth. The focal point of this paper is to break down the utilization of data mining in medical space and a portion of the systems utilized in critical disease prediction. We have completely reviewed many research papers of data mining identified with some critical disease prediction.

scholarly journals Digital Wellness: A Smart Health Care System Using Machine Learning

2021 ◽  
Vol 10 (6) ◽  
pp. 5-11
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Algorithm ◽  
Medical Science ◽  
Medical Data ◽  
Accurate Prediction ◽  
Disease Prediction ◽  
Accurate Analysis ◽  
Disease Symptoms ◽  
Pattern Information

Nowadays,people face various diseases due to environmental condition and their living habits. So the prediction of disease at an earlier stage becomes an important task. But the accurate prediction based on symptoms becomes too difficult for the doctor. The correctprediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has a large amount of data growth per year. Due to the increasing amount of data growth in the medicaland healthcare field the accurate analysis of medical data has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in a huge amount of medical data. We proposed general disease prediction based on the symptoms of the patient. For the disease prediction, we use Convolutional neural network (CNN) machine learning algorithm for the accurate prediction of disease. For disease prediction required disease symptoms dataset. After general disease prediction, this system able to gives the risk associated with a general disease which is a lower risk of general disease or highe

scholarly journals Indepth Analysis of Medical Dataset Mining: A Comparitive Analysis on a Diabetes Dataset Before and After Preprocessing

2019 ◽  
Author(s):  
Latifa Nass ◽  
Stephen Swift ◽  
Ammar Al Dallal
Keyword(s):  
Data Mining ◽  
Error Rate ◽  
Missing Values ◽  
Graphical Representation ◽  
Classification Performance ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Data Set ◽  
Medical Dataset

Most of the healthcare organizations and medical research institutions store their patient’s data digitally for future references and for planning their future treatments. This heterogeneous medical dataset is very difficult to analyze due to its complexity and volume of data, in addition to having missing values and noise which makes this mining a tedious task. Efficient classification of medical dataset is a major data mining problem then and now. Diagnosis, prediction of diseases and the precision of results can be improved if relationships and patterns from these complex medical datasets are extracted efficiently. This paper analyses some of the major classification algorithms such as C4.5 ( J48), SMO, Naïve Bayes, KNN Classification algorithms and Random Forest and the performance of these algorithms are compared using WEKA. Performance evaluation of these algorithms is based on Accuracy, Sensitivity and Specificity and Error rate. The medical data set used in this study are Heart-Statlog Medical Data Set which holds medical data related to heart disease and Pima Diabetes Dataset which holds data related to Diabetics. This study contributes in finding the most suitable algorithm for classifying medical data and also reveals the importance of preprocessing in improving the classification performance. Comparative study of various performances of machine learning algorithms is done through graphical representation of the results. Keywords: Data Mining, Health Care, Classification Algorithms, Accuracy, Sensitivity, Specificity, Error Rate

scholarly journals Use of Data Mining Technique for Prediction of Crop Disease & Yield in The Face of Climate Change and Market Analysis Strategy

2019 ◽  
pp. 126-131
Author(s):  
Pallavi Chature ◽  
Pallavi Borde ◽  
Rohini Khemnar ◽  
Sonal Dhokane ◽  
N. B. Kadu
Keyword(s):  
Data Mining ◽  
Crop Production ◽  
Crop Productivity ◽  
Disease Prediction ◽  
Nearest Neighbour ◽  
Data Mining Technique ◽  
The Future ◽  
The Face ◽  
Crop Disease ◽  
Social Use

Data mining and machine gaining knowledge of is an emerging field of research in facts era in addition to in agriculture. Agrarian sector is facing rigorous trouble to maximize the crop productiveness. The present have a look at makes a specialty of the packages of data mining strategies in crop sickness prediction in the face of climatic trade to assist the farmer in taking choice for farming and accomplishing the predicted monetary go back. The Crop disease prediction is a prime hassle that may be solved based totally on available data. Data mining strategies are the better selections for this purpose. Exclusive data mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. The main cause of the gadget is for social use. Farmer has to face many troubles like lack of know-how, Manures, fertilizers and Agriculture marketing etc. gift method SAR Tomography takes the photographs and gives the exceptional development stages of crop. This system not gives the fertilizers and precautions to the farmer. This paper gives quick analysis of crop disease prediction the usage of k Nearest Neighbour class approach and Density based clustering approach for the chosen place. The styles of crop production in response to the climatic (rainfall, temperature, relative humidity and sunshine) impact across the selected regions are being evolved using ok Nearest Neighbour technique. For that reason, it is going to be useful if farmers should use the technique to are expecting the future crop productivity and therefore adopt opportunity adaptive measures to maximize yield if the predictions fall below expectations and business viability.

Using the Fuzzy Logic to Find Optimal Centers of Clusters of K-means

2016 ◽  
Vol 6 (6) ◽  
pp. 3068
Author(s):  
Wed Kadhim Oleiwi
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Heart Disease ◽  
Real World ◽  
Medical Diagnosis ◽  
Medical Data ◽  
World Health ◽  
Medical Field ◽  
Healthcare Data ◽  
Field Researcher

<p>Techniques of data mining that used in the medical diagnosis a number of diseases like cancer, diabetes, stroke, and heart disease. The great importance emerging fields for providing diagnosis and a profounder understanding of medical data, its coms from Data mining in medical field .researcher attempts to solve real world health problems in the prognosis and treatment of diseases, by using Healthcare data mining. In this research, the algorithm of k-means is used for grouping medical data, the problem of k-means is to find optimal centers of clusters so, and fuzzy logic is used to get optimal centers of clusters.</p>

scholarly journals Data Mining in Cancer Diagnosis and Prediction: Review about Latest Ten Years

2020 ◽  
pp. 11-32
Author(s):  
Zahraa Naser Shah Weli
Keyword(s):  
Data Mining ◽  
Cancer Detection ◽  
Lower Cost ◽  
Medical Data ◽  
Medical Field ◽  
Clinical Prognosis ◽  
Cancer Prediction ◽  
Hidden Patterns ◽  
Vague Data

Data Mining [DM] has exceptional and prodigious potential for examining and analyzing the vague data of the medical domain. Where these data are used in clinical prognosis and diagnosis. Nevertheless, the unprocessed medical data are widely scattered, diverse in nature, and voluminous. These data should be accumulated in a sorted out structure. DM innovation and creativity give a customer a situated way to deal with new fashioned and hidden patterns in the data. The advantages of using DM in medical approach are unbounded and it has abundant applications, the most important: it leads to better medical treatment with a lower cost. Consequently, DM algorithms have the main usage in cancer detection and treatment through providing a learning  rich environment which can help to improve the quality of clinical decisions. Multi researches are published about the using of DM in different destinations in the medical field. This paper provides an elaborated study about utilization of DM in cancer prediction and classifying, in addition to the  main features and challenges in these researches are introduced in this paper for helping  apprentice and youthful scientists and showing for them the key principle issues that are still exist around there.

scholarly journals Using the Fuzzy Logic to Find Optimal Centers of Clusters of K-means

2016 ◽  
Vol 6 (6) ◽  
pp. 3068
Author(s):  
Wed Kadhim Oleiwi
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Heart Disease ◽  
Real World ◽  
Medical Diagnosis ◽  
Medical Data ◽  
World Health ◽  
Medical Field ◽  
Healthcare Data ◽  
Field Researcher

<p>Techniques of data mining that used in the medical diagnosis a number of diseases like cancer, diabetes, stroke, and heart disease. The great importance emerging fields for providing diagnosis and a profounder understanding of medical data, its coms from Data mining in medical field .researcher attempts to solve real world health problems in the prognosis and treatment of diseases, by using Healthcare data mining. In this research, the algorithm of k-means is used for grouping medical data, the problem of k-means is to find optimal centers of clusters so, and fuzzy logic is used to get optimal centers of clusters.</p>

scholarly journals An Efficient Technique for Disease Prediction by Using Enhanced Machine Learning Algorithms for Categorical Medical Dataset

2021 ◽  
Vol 50 (1) ◽  
pp. 102-122
Author(s):  
Veera Anusuya ◽  
V Gomathi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Large Datasets ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Disease Prediction ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Prediction Mechanism ◽  
Medical Dataset

In the 20th century, it is evident that there is a massive evolution of chronic diseases. The data mining approaches beneficial in making some medicinal decisions for curing diseases. But medical data may consist of a large number of data, which makes the prediction process a very difficult one. Also, in the medical field, the dataset may involve both the small database and extensive database. This creates the study of a complex one for disease prediction mechanism. Hence, in this paper, we intend to use a practical machine learning approach for disease prediction of both large and small datasets. Among the various machine learning procedures, classification, and clusters method play a significant role. Therefore, we introduced the enhanced classification and clusters approach in this work for obtaining better accuracy results for disease prediction. In this proposed method, a process of preprocessing is involved, followed by Eigen vector extraction, feature selection, and classification Further, the most suitable features are selected with the use of Multi-Objective based Ant Colony Optimization (MO-ACO) from the extracted features for increasing the classification and clusters. Here we have shown the novelty in every stage of the implementation, such as feature selection, feature extraction, and the final prediction stage. The proposed method will be compared with the existing technique on the measure of precision, NMI, execution time, recall, and Accuracy. Here we conclude with the solution having more accuracy for both small and large datasets.

scholarly journals Data Mining Approach in Preterm Birth Prediction

2010 ◽  
Vol 9 (1) ◽  
pp. 18-30 ◽  
Author(s):  
Jyothi Thomas ◽  
G. Kulanthaivel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Preterm Birth ◽  
Field Data ◽  
Risk Groups ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Mining Approach ◽  
Health Field

Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering.

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