Crime analysis in India using data mining techniques

An approach for crime detection in India using Data mining techniques is proposed in this paper. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Data mining techniques are often applied to Criminology as it provides good results. Criminology is a field which studies about various crime characteristics. Analyzing crime data means exploring crime data. Crime is identified using k-means clustering and the clusters are formed based on the similarity of the crime attributes. The Random Forest algorithm and Neural networks are applied on the data for classification. Visualization is achieved using the Google marker clustering and the crime spots are marked on the India map. The accuracy is verified using WEKA tool. This approach will benefit the Crime department of India in analyzing crime with better prediction. The paper focuses on the crime analysis of various Indian states and union territories during 2001 to 2012.

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

International Journal of Informatics and Communication Technology (IJ-ICT) ◽

10.11591/ijict.v8i1.pp56-62 ◽

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>

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

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2675-2681 ◽

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>

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Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h7168.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 963-967

Keyword(s):

Data Mining ◽

Heart Disease ◽

Random Forest ◽

Early Diagnosis ◽

Decision Tree ◽

Heart Diseases ◽

Classification Algorithms ◽

Random Forest Algorithm ◽

Medical Field ◽

Data Mining Techniques

Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.

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