Approaches to Partition Medical Data using Clustering Algorithms

Kalyani, P.

doi:10.5120/7941-1102

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…Empirical results are obtained by comparing different clustering algorithms. These results are used to study independence or correlation between diseases and for better insight into medical survey data [5]. Partitional clustering on ILPD dataset is implemented using Kmeans algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder

Pathan¹

2018

IJRASET

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Data mining techniques can be applied in various fields such as Information Retrieval, Business analytics, Medicine and many more. This paper deals with medical field which mainly focuses on liver disease diagnoses. The aim of this study is to implement different classification algorithms on Indian Liver Patient Dataset (ILPD) using WEKA in order to get proper prediction of liver disorders. Feature selection is carried out on the dataset. Pre-processing is carried out to pre-process and cluster the data. K means clustering algorithm is used for pre-processing the data. The clustered data is further applied to various classification algorithms such as Naive Bayes, Ada Boost, J48, Bagging and Random Forest. A comparison is carried out considering performance measures such as Accuracy, Error rate, Precision, Recall and F measure. On the basis of comparison, the results are concluded. Random Forest algorithm provides best performance among all.

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Section: Literature Surveymentioning

confidence: 99%

Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder

Pathan¹

2018

IJRASET

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“…It may be a powerful tool and probably will be used increasingly more in medical research. 29,30 We believe that the patients included in the present study may be considered a representative sample of the osteoarthritic population because the age, sex, BMI, and coronal FTMA match with the patients requiring knee replacement. 31 The conclusions are therefore applicable, at least in an investigational frame.…”

Section: Discussionmentioning

confidence: 92%

Dynamic Alignment Analysis in the Osteoarthritic Knee Using Computer Navigation

et al. 2017

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The lower limb alignment is influenced by the geometry of the joint surfaces and surrounding soft tissue tension. The mechanical behavior changes in a normal, osteoarthritic, and postoperative knee. The purpose of this study is to determine the dynamic coronal femoral tibial mechanical angle (FTMA) in osteoarthritic knees using computer navigation. The authors hypothesize that there are different varus-valgus patterns between flexion and extension in the osteoarthritic knee. We conducted a transversal observational study and included patients with osteoarthritis who underwent primary navigation TKA (Orthopilot version 4.2; B. Braun Aesculap, Tuttlingen, Germany). In total, 98 consecutive patients with 100 osteoarthritic knee joints, on which total knee arthroplasty was performed in our institution from 2009 to 2010, were enrolled in this prospective study. The FTMA was measured with the patient supine with maximum knee extension possible (considering the value as 0), 30, 60, and 90 degrees. All FMTA data obtained were segmented by hierarchic cluster measuring method. Through the clustering system, five segments were generated for varus patients and three for valgus patients: expected varus, expected valgus, severe varus, severe valgus, structured varus, structured valgus, concave varus, mixed varus-valgus, and mixed valgus-varus. The findings of the present study have demonstrated that there is a well-defined dynamic alignment in osteoarthritic knees, resulting in a wide kinematic variation in the coronal FTMA between flexion and full extension. Further studies will be necessary to determine whether this dynamic approach to FTMA has clinical utility in the surgeon's decision-making process.

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“…Data mining applications are useful for commercial and scientific sides [2]. In healthcare application, it is an important method that can be used to detect unknown diseases [3] and identify effective treatments [4]. Data mining technique can be classified into two categories: Supervised and Unsupervised learning [5].…”

Section: Introductionmentioning

confidence: 99%

OCA: overlapping clustering application unsupervised approach for data analysis

Danganan

Sison

Medina

2019

IJEECS

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<p>In this paper, a new data analysis tool called Overlapping Clustering Application (OCA) was presented. It was developed to identify overlapping clusters and outliers in an unsupervised manner. The main function of OCA is composed of three phases. The first phase is the detection of the abnormal values(outliers) in the datasets using median absolute deviation. The second phase is to segment data objects into cluster using k-means algorithm. Finally, the last phase is the identification of overlapping clusters, it uses maxdist (maximum distance of data objects allowed in a cluster) as a predictor of data objects that can belong to multiple clusters. Experimental results revealed that the developed OCA proved its capability in detecting overlapping clusters and outliers accordingly.</p>

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Approaches to Partition Medical Data using Clustering Algorithms

Cited by 12 publications

References 2 publications

Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder

Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder

Dynamic Alignment Analysis in the Osteoarthritic Knee Using Computer Navigation

OCA: overlapping clustering application unsupervised approach for data analysis

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