Using Data Mining Techniques to Predict Diabetes and Heart Diseases

2018 ◽  
Author(s):  
Ammar Aldallal ◽  
Amina Abdul Aziz Al-Moosa
Keyword(s):  
Data Mining ◽  
Heart Diseases ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals Predicting heart ailment in patients with varying number of features using data mining techniques

2019 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span>Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

scholarly journals Predicting Heart Ailment in Patients with Varying number of Features using Data Mining Techniques

2019 ◽  
Vol 9 (4) ◽  
pp. 2675
Author(s):  
T R Stella Mary ◽  
Shoney Sebastian
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All

<span lang="EN-US">Data mining can be defined as a process of extracting unknown, verifiable and possibly helpful data from information. Among the various ailments, heart ailment is one of the primary reason behind death of individuals around the globe, hence in order to curb this, a detailed analysis is done using Data Mining. Many a times we limit ourselves with minimal attributes that are required to predict a patient with heart disease. By doing so we are missing on a lot of important attributes that are main causes for heart diseases. Hence, this research aims at considering almost all the important features affecting heart disease and performs the analysis step by step with minimal to maximum set of attributes using Data Mining techniques to predict heart ailments. The various classification methods used are Naïve Bayes classifier, Random Forest and Random Tree which are applied on three datasets with different number of attributes but with a common class label. From the analysis performed, it shows that there is a gradual increase in prediction accuracies with the increase in the attributes irrespective of the classifiers used and Naïve Bayes and Random Forest algorithms comparatively outperforms with these sets of data.</span>

scholarly journals Human Heart Disease Prediction System Using Data Mining Techniques

2019 ◽  
Vol 8 (II) ◽  
Author(s):  
Sidra Javed ◽  
Hamza Javed ◽  
Ayesha Saddique ◽  
Beenish Rafiq
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Heart Diseases ◽  
Heavy Load ◽  
Data Mining Techniques ◽  
Id3 Algorithm ◽  
Wide Range ◽  
Using Data ◽  
Human Heart Disease

— Prediction of heart disease is a big concern now a days because everyone is busy and due to heavy load of work people do not give attention to their health. To diagnose a disease is a big challenge. The issue is to extract data that have some meaningful knowledge. For this purpose, data mining techniques are used to extract meaningful data. Decision Tree and ID3 are used to predict heart diseases. Many researchers and practitioners are familiar with prediction of heart diseases and wide range of techniques is available to predict disease. To address this problem, Decision Tree is used to predict the heart disease. In this study the collected data is pre-processed, Decision Tree algorithm and ID3 were then applied to predict the heart disease.   Index Terms— Decision Tree, ID3 Algorithm, Data Mining, Decision Support System (DSS), knowledge Discovery from Databases (KDD).

scholarly journals Heart Diseases Forecast using Data Mining Techniques and Tools

2019 ◽  
Vol 9 (2) ◽  
pp. 2726-2729
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Heart Diseases ◽  
Complex Data ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Statistical Knowledge ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Using Data

Data Mining have always been a field and combination of both computer science and statistical knowledge. From the beginning it is used to ascertain designs, patterns and arrangements which are formed in the information pool. The motive of the data mining development is to produce useful information from the pool of raw data and convert it into useful information which can be used for future arrangements. The tools which are used in data mining are helpful in predicting the future trends and predictions across the market, which also help in decision making and building the knowledge to make decisions. The “Healthcare Industry” is generally information rich. It has been collecting data to improve the continuing problems and help to identify the solutions for that problems. Data mining techniques can be used to predict heart conditions from the voluminous and complex data which are kept by the hospitals for decision making which are difficult to analyze by outmoded methods. Unfortunately, outmoded methods are less accurate in discovering hidden information from effective decision making. Data mining helps in altering the huge amount of data into knowledge driven which takes, as compared to others, less time and effort for the prediction and with greater accuracy. Our effort is to apply different data mining techniques that are used to solve the problem of biased forecasts and decision making and help in calculating the results with more accuracy.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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