Prediction of Heart Disease using Naïve Bayes Technique of Data Mining

Coronary illness is responsible for deaths in all age groups and is common among males and females. An excellent answer for this issue is to have the option to predict what a patient's health status will in future so the specialists can begin treatment much sooner which will yield better outcomes. Data mining plays most significant role in area of investigation by means of the objective to finding essential data from massive amount of information. Currently, data mining strategies and tools are utilized by researchers in the field of healthcare, especially for prediction of sickness. Data mining methodology affords improvement approach to interchange huge data into beneficial information for attaining selection. In utilising data mining patterns they desires considerably fewer amount of funding intended for the forecasting the ailment alongside better accurate and precision. Moreover, analysis of study paper depicts the estimation of coronary illness in clinical field by utilizing data mining. Various popular data mining algorithm on the dataset of 13 attributes is applied to forecast the coronary ailment at initial stage. The dataset is collected from UCI machine learning repository and analysed with various parameters like Accuracy, Recall, Precision, F-measure, ROC area and Kappa statistics. Experimental results show that the Naïve bayes algorithm is always becomes the best-performing data mining method which accomplishes an accuracy of 86.716% in coronary illness prediction.

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Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v3i2.77 ◽

2019 ◽

Vol 3 (2) ◽

pp. 59

Author(s):

Munawir Munawir ◽

Taufiq Iqbal

Keyword(s):

Data Mining ◽

Research Method ◽

Naive Bayes ◽

Naïve Bayes ◽

Data Mining Algorithm ◽

Standard Data ◽

Industry Standard ◽

Bayes Algorithm ◽

Using Data ◽

Banda Aceh

The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm.

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Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

Jurnal ULTIMATICS ◽

10.31937/ti.v12i2.1715 ◽

2020 ◽

Vol 12 (2) ◽

pp. 104-107

Author(s):

Nurhayati . ◽

Nuraeny Septianti ◽

Nani Retnowati ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Information Technology ◽

Data Processing ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Processing Data ◽

Student Graduation ◽

Phase Data ◽

Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

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Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes

Indonesian Journal of Applied Informatics ◽

10.20961/ijai.v5i1.44379 ◽

2021 ◽

Vol 5 (1) ◽

pp. 32

Author(s):

Hartatik Hartatik

Keyword(s):

Data Mining ◽

Big Data ◽

Prediction Models ◽

Naive Bayes ◽

Program Planning ◽

Naïve Bayes ◽

Bayes Method ◽

Student Graduation ◽

Bayes Algorithm ◽

Naive Bayes Method

Abstrak :Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma naive bayes. Algoritma Naive bayes merupakan salah satu metode yang digunakan untuk memprediksi kelulusan mahasiswa. Peneliti dalam hal ini menerapkan metode Naive bayes menggunakan parameter Indeks prestasi kumulatif( IPK) dan membandingkan dengan menggunakan prediksi naive bayes methods berdasarkan parameter IPK dan sosial parameter yaitu jenis kelamin dan status tinggal. Dalam penelitian ini menggunakan parameter akademis dan dilakukan optimasi menggunakan parameter sosial yang melekat pada mahasiswa. Berdasarkan hasil evaluasi untuk mendapatkan akurasi, hasil dari penelitian ini mendapatkan nilai akurasi untuk metode Naive bayes sebesar 75% dan akurasi untuk model prediksi dengan parameter sosial sebesar 85% dengan selisih akurasi 10%.__________________________Abstract : Predictions about a student's graduation status are a problem in college. Major tertiary institutions in the era of Big Data are very important to predict the behavior of active students so that they can find out the possibility of students in a timely manner and can determine preventive steps in making program planning. One method used is data mining techniques using the Naive bayes Algorithm. The Naive bayes algorithm is one of the methods used to predict student graduation. Researchers in this case applied the Naive bayes method using the cumulative achievement index (GPA) parameter and compared using the prediction of the Naive bayes method based on the GPA parameters and social parameters, namely gender and status. This study uses academic parameters and is carried out optimally using social parameters inherent in students. Based on the results of the evaluation to get an accuracy value, the results of this study get an accurate value for the Naive bayes method of 75% and accurate for prediction models with social parameters of 85% with a difference of 10%.

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Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

Journal of Innovation Information Technology and Application (JINITA) ◽

10.35970/jinita.v1i01.64 ◽

2019 ◽

Vol 1 (01) ◽

pp. 25-34

Author(s):

Ade Riani ◽

Yessy Susianto ◽

Nur Rahman

Keyword(s):

Data Mining ◽

Heart Rate ◽

Heart Disease ◽

Chest Pain ◽

Naive Bayes ◽

Naïve Bayes ◽

Mining Method ◽

The World ◽

Bayes Algorithm ◽

Exercise Induced

Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested.

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The Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Tech-E ◽

10.31253/te.v4i2.543 ◽

2021 ◽

Vol 4 (2) ◽

pp. 44

Author(s):

Rino Rino

Keyword(s):

Data Mining ◽

Heart Disease ◽

Naive Bayes ◽

Naïve Bayes ◽

Data Set ◽

A Value ◽

C4.5 Algorithm ◽

Calculation Results ◽

Mining Methods ◽

Bayes Algorithm

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the Naïve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the Naïve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the Naïve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the Naïve Bayes algorithm in predicting heart disease.

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Analisis Kelayakan Lokasi Promosi Dalam Penerimaan Mahasiswa Baru (PMB) Dengan Algoritma Naïve Bayes & Decission Tree C4.5

Kilat ◽

10.33322/kilat.v10i1.1196 ◽

2021 ◽

Vol 10 (1) ◽

pp. 169-178

Author(s):

Wulan Wulandari

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Algorithms ◽

Tertiary Institution ◽

Public And Private ◽

Measurement Results ◽

Bayes Algorithm ◽

The City ◽

New Student

Competition for new student admissions in every public and private tertiary institution is currently growing rapidly every year, some spend a lot of money on promotional activities, to assist institutions / institutions in obtaining recommendations for the feasibility of promotion locations based on several measurement criteria using the classification algorithms contained in data mining . The algorithm used to compare the measurement of the feasibility of the promotion location of the city and district of Bekasi is Naïve Bayes and Decission Tree C4.5 using four parameters including the number of students in one sub-district, the number of students in one sub-district, the distance of location and last year's enthusiasts using 35 regions / sub-districts in Bekasi city and district. measurement results using the rapidminner, the accuracy value of the Naïve Bayes algorithm is 91.43% and the Decission Tree C4.5 is 94.29%.

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Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020731907 ◽

2020 ◽

Vol 7 (4) ◽

pp. 737

Author(s):

Sitti Aliyah Azzahra ◽

Arief Wibowo

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Emotional Expressions ◽

Tourist Attraction ◽

Test Results ◽

Tourist Attractions ◽

Bayes Algorithm ◽

Labeling Method ◽

The City

Wisatawan seringkali mencari informasi tentang obyek wisata pada situs web seperti TripAdvisor. Situs web TripAdvisor memiliki fitur bagi penguna terdaftar untuk memberi ulasan tentang objek wisata dalam kategori kuliner dari berbagai negara. Ulasan tersebut bisa digunakan wisatawan sebagai pertimbangan sebelum mendatangi objek wisata kuliner yang ingin dituju. Komentar atau ulasan yang ada di situs TripAdvisor dapat dianalisis untuk mengetahui nilai sentimen dari suatu obyek wisata yang diulas. Hasil analisis itu dapat bermanfaat bagi pengelola tempat wisata, pengusaha kuliner maupun bagi wisatawan lain. Ada tantangan yang ditemukan saat analisis sentimen dilakukan pada kalimat ulasan yang mengandung ikon emosi atau emoticon, karena ulasan dapat mengandung arti sentimen yang berbeda antara kalimat dengan ekspresi emosi yang ada. Penelitian ini berisi analisis ulasan tentang kuliner kota Bandung pada situs TripAdvisor yang mengklasifikasi sentimen menjadi tiga kelas. Penelitian ini menggunakan teknik klasifikasi data mining dengan algoritme Naïve Bayes dikombinasi dengan metode pelabelan multi aspek yang disertai konversi ikon emosi pada teks ulasan. Selain itu, analisis dilakukan pada bobot ulasan berdasarkan jumlah kontribusi pemberi ulasan di web TripAdvisor. Hasil pengujian menunjukkan bahwa penggunaan seluruh kombinasi metode tersebut dalam proses klasifikasi sentimen mampu menghasilkan nilai akurasi sebesar 98,67%. AbstractTourists often look for information about attractions on websites such as TripAdvisor. The TripAdvisor website has a feature for registered users to provide reviews about attractions in the culinary category from various countries. These reviews can be used by tourists as a consideration before visiting culinary attractions to be addressed. Comments or reviews on the TripAdvisor site can be analyzed to determine the sentiment value of a tourist attraction being reviewed. The results of the analysis can be useful for managers of tourist attractions, culinary entrepreneurs and for other tourists. There are challenges that are found when sentiment analysis is carried out on review sentences that contain emotion icons or emoticons, because reviews may contain different sentiment meanings between sentences and existing emotional expressions. This study contains a review of the culinary analysis of the city of Bandung on the TripAdvisor site which classifies sentiments into three classes. This study uses data mining classification techniques with the Naïve Bayes algorithm combined with a multi-aspect labeling method accompanied by the conversion of emotional icons in the review text. In addition, the analysis is carried out on the weight of the review based on the number of contributing reviewers on the TripAdvisor web. The test results show that the use of all combinations of these methods in the sentiment classification process is able to produce an accuracy value of 98.67%.

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Migrating From Data Mining to Big Data Mining

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.4.14667 ◽

2018 ◽

Vol 7 (3.4) ◽

pp. 13

Author(s):

Gourav Bathla ◽

Himanshu Aggarwal ◽

Rinkle Rani

Keyword(s):

Data Mining ◽

Big Data ◽

Response Time ◽

Large Scale ◽

Naive Bayes ◽

Naïve Bayes ◽

Data Mining Algorithm ◽

Big Data Mining ◽

Data Mining Algorithms ◽

Mining Algorithms

Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.

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Comparison of Open Source Data Mining Tools: Naive Bayes Algorithm Example

2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) ◽

10.1109/ebbt.2019.8741664 ◽

2019 ◽

Keyword(s):

Data Mining ◽

Open Source ◽

Naive Bayes ◽

Naïve Bayes ◽

Open Source Data ◽

Source Data ◽

Bayes Algorithm ◽

Mining Tools

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Adatbányászati technikák alkalmazása magyar vállalkozások adatait tartalmazó adatbázison Microsoft Excel 2007-ben

Jelenkori Társadalmi és Gazdasági Folyamatok ◽

10.14232/jtgf.2010.1-2.229-233 ◽

2010 ◽

Vol 5 (1-2) ◽

pp. 229-233

Author(s):

György Hampel ◽

Zoltán Fabulya ◽

Elemérné Nagy

Keyword(s):

Data Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

The Other ◽

Annual Income ◽

Data Mining Technique ◽

Microsoft Excel ◽

Mining Technique ◽

Bayes Algorithm ◽

Main Activity

Using a simple data mining technique, the Analyze Key Influencers, in Excel 2007 Data Mining Add-ins, we searched for relationship among the seat (county and town), the form of business, the main activity, the number of employees and the annual income of the Hungarian companies. This technique uses the Naive Bayes algorithm. According to the used method the seat has no influencers. Most of the main activities have no influencers, but some activities (82 out of 495) have relationship with the other criteria, mainly with the form of business. The form of business (all 30 categories), the number of employees (17 of 18 categories) and the annual income (all 9 categories) are each others key influencers. Cramer's association was used to check the results of the data mining. The Cramer contin-gency coefficient showed similar results as the data mining, but the results also indicated that the strength of the association was less than moderate in all cases. The highest associa-tion were between the annual income and the number of employees (0.46, moderate asso-ciation), the main activity and form of business (0.36, moderate association) and the annual income and the form of business (0.27, low association).

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