Diabetes Prediction using Data Mining

Background: Diabetes mellitus (DM) is one of the most common diseases in the world. Complications of this disease include nephropathy, cardiac arrest, blindness, and even mutilation of the body. The accurate diagnosis of this condition is very important. Objectives: This study was to identify and provide a model for diagnosis of DM using data mining. Methods: The data used in this study were obtained from 768 women aged 21-83 year old. Nine variables were selected for investigation. The neural network, Basin network, C5.0, and support vector machine models were compared for predicting diabetes and their precision to this end. Clementine 12 software was used to analyze the data. Results: The proposed method for classification of records with the C5.0 algorithm for accuracy data is 80.2% and for accuracy data 87.5%. In comparison with similar studies, it was better to diagnose people with diabetes, while glucose, body mass index and age variables were important in this study. Conclusion: The C5.0 algorithm showed the highest value of accuracy, specificity, and sensitivity compared with other methods studied. Therefore, the C5.0 algorithm probably performs the best classification among other algorithms and is recommended as the best method for diabetes prediction using available data.

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Diabetes prediction using Data mining Classification Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4735.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 5901-5905

Keyword(s):

Data Mining ◽

American Diabetes Association ◽

Medical Information ◽

Classification Techniques ◽

Technology Advancement ◽

The World ◽

Diabetes Prediction ◽

Decision Tress ◽

Using Data ◽

Test Result

Diabetes is one of the second largest disease in the world. In the recent survey it shows that there are overall 246 million people affected with this and in that women ratio is more. By the report of WHO, this figure is going to reach to 380 million by 2025. According to the American Diabetes Association,6% of the population are not aware that there are victims of diabetes and also every 21 sec at least for an individual diabetic test result is positive. With the technology advancement in the field of medical information, data is well maintained in the databases. This paper focuses on to diagnose data to provide the solution by observing the patterns in the data using various datamining classification techniques such as Naïve basis, Logistic regression, Decision tress etc

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A Hybrid Approach for Diabetes Prediction and Risk Analysis Using Data Mining

Lecture Notes in Electrical Engineering - Advances in Communication and Computational Technology ◽

10.1007/978-981-15-5341-7_92 ◽

2020 ◽

pp. 1213-1230

Author(s):

Bhavna ◽

Rupali Verma ◽

Rohit Handa ◽

Veena Puri

Keyword(s):

Data Mining ◽

Risk Analysis ◽

Hybrid Approach ◽

Diabetes Prediction ◽

Using Data

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A Study on Detection of Small Size Malicious Code using Data Mining Method

Jouranl of Information and Security ◽

10.33778/kcsa.2019.19.1.011 ◽

2019 ◽

Vol 19 (1) ◽

pp. 11-17

Author(s):

Taek-Hyun Lee ◽

◽

Ho Kook Kwang

Keyword(s):

Data Mining ◽

Malicious Code ◽

Mining Method ◽

Data Mining Method ◽

Using Data

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A Study on the Analysis of Employment Decision Factor of the Visually Impaired using Data Mining Technique

Disability & Employment ◽

10.15707/disem.2013.23.1.011 ◽

2013 ◽

Vol 23 (1) ◽

pp. 273-302 ◽

Cited By ~ 6

Author(s):

임은정 ◽

신현욱 ◽

김성진

Keyword(s):

Data Mining ◽

Visually Impaired ◽

Data Mining Technique ◽

Mining Technique ◽

Employment Decision ◽

Using Data

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

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.468 ◽

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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Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

Creative Information Technology Journal ◽

10.24076/citec.2014v1i4.27 ◽

2015 ◽

Vol 1 (4) ◽

pp. 270

Author(s):

Muhammad Syukri Mustafa ◽

I. Wayan Simpen

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Test Results ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Sample Data ◽

New Students ◽

K Nearest Neighbor Algorithm ◽

Using Data ◽

Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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