Predicting the medical data by using different algorithms
Farklı algoritmalar kullanarak tıbbi verilerin tahmini
- Tez No: 769014
- Danışmanlar: Assist. Prof. Dr. SEFER KURNAZ
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Kalp hastalıkları, Lojistik regresyon yöntemi, Naive Bayes sınıflandırıcısı, Veri madenciliği, Yapay sinir ağları, Heart diseases, Logistic regression method, Naive Bayes classifier, Data mining, Artificial neural networks
- Yıl: 2022
- Dil: İngilizce
- Üniversite: Altınbaş Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Bilişim Teknolojileri Ana Bilim Dalı
- Bilim Dalı: Bilişim Teknolojileri Bilim Dalı
- Sayfa Sayısı: Belirtilmemiş.
Özet
The healthcare environment produces very large data, so it needs many effective and important roles in order to extract and analyze data as well as access to knowledge. Previous researchers have applied many statistical tools with data mining tools in addition to enhancing the number of data methods in regards of extraction and analysis. The first and only aspect that confirms the efficacy and resilience of this exploration tool in the healthcare context is the diagnosis procedure for sensitive illness. In past few years, heart disease has been proven as the top cause of mortality globally, according to WHO data. The goal of researchers' use of statistical methods and health information analysis is to aid professionals in creating the correct judgment possible in the procedure of forecasting risk in patients and accurately diagnosing it. We used real data on a group of patients, and their number was 665 for each patient, divided into two parts: 300 for males, and 365 for females, with 19 different traits (reduced to 10 related characteristics). Our aims at two things: the first is to process medical data and purify it in different ways in order to access knowledge and make the appropriate decision that helps decision-makers through the classification analysis process, and the second is the process of applying a set of appropriate to use data samples in order to arrive at precise understanding and proper illness prognosis the human heart We evaluated by comparing our findings to those of a separate demographic of past study in needed to arrive at the highest utility and select the most effective method in the method to evaluate medical data and making predictions about heart problems, so we evaluated by comparing 3 main techniques: the logistic regression, Bayesian classification, and networks. The neurologic results were (98.85%, 98.16%), and (91.31%), correspondingly. Finally, our major aim is to improve diagnosis by obtaining strong outcomes with high accuracy in diagnosing and forecasting the present.
Özet (Çeviri)
The healthcare environment produces very large data, so it needs many effective and important roles in order to extract and analyze data as well as access to knowledge. Previous researchers have applied many statistical tools with data mining tools in addition to enhancing the number of data methods in regards of extraction and analysis. The first and only aspect that confirms the efficacy and resilience of this exploration tool in the healthcare context is the diagnosis procedure for sensitive illness. In past few years, heart disease has been proven as the top cause of mortality globally, according to WHO data. The goal of researchers' use of statistical methods and health information analysis is to aid professionals in creating the correct judgment possible in the procedure of forecasting risk in patients and accurately diagnosing it. We used real data on a group of patients, and their number was 665 for each patient, divided into two parts: 300 for males, and 365 for females, with 19 different traits (reduced to 10 related characteristics). Our aims at two things: the first is to process medical data and purify it in different ways in order to access knowledge and make the appropriate decision that helps decision-makers through the classification analysis process, and the second is the process of applying a set of appropriate to use data samples in order to arrive at precise understanding and proper illness prognosis the human heart We evaluated by comparing our findings to those of a separate demographic of past study in needed to arrive at the highest utility and select the most effective method in the method to evaluate medical data and making predictions about heart problems, so we evaluated by comparing 3 main techniques: the logistic regression, Bayesian classification, and networks. The neurologic results were (98.85%, 98.16%), and (91.31%), correspondingly. Finally, our major aim is to improve diagnosis by obtaining strong outcomes with high accuracy in diagnosing and forecasting the present.
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