Forecasting of Heart Diseases in Early Stages Using Machine Learning Approaches

Jha, Khushi; Jha, Ankita Kumari; Rathore, Kuldeep Singh; Mahesh, T R

doi:10.1109/fabs52071.2021.9702665

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…On EMR databases, various machine learning techniques are used to search for hidden patterns that aid in cancer diagnosis [20]. Deep learning neural networks are utilized to evaluate CT and MRI scans, and natural language processing (NLP) is used to interpret doctor's prescriptions [21][22][23][24]. Big data and machine learning can be used for diagnosis.…”

Section: Machine Learningmentioning

confidence: 99%

Early Detection of Cancer using Machine Learning (ML) Techniques

Mahesh

Kumar²

2023

International Journal of Information Technology, Research and A

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. A combination of expert AI and highlight determination calculations produces improved results in the early detection and forecast of oral cancer. The main goal and commitment of this audit study is to summarize the use of AI technologies for accurate early prediction of oral malignant development.

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Section: Machine Learningmentioning

confidence: 99%

Early Detection of Cancer using Machine Learning (ML) Techniques

Mahesh

Kumar²

2023

International Journal of Information Technology, Research and A

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“…Single points of failure, altered signatures, and encoded traffic are a few instances of inline functionality that could be problematic. Using IDS, the system's performance is evaluated [22][23][24][25].…”

Section: Figure 1 Intrusion Detection Systemmentioning

confidence: 99%

Implementation of Machine Learning-Based Data Mining Techniques for IDS

Mahesh

Vivek

Kumar

2023

International Journal of Information Technology, Research and A

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The internet is essential for ongoing contact in the modern world, yet its effectiveness might lessen the effect known as intrusions. Any action that negatively affects the targeted system is considered an intrusion. Network security has grown to be a major issue as a result of the Internet's rapid expansion. The Network Intrusion Detection System (IDS), which is widely used, is the primary security defensive mechanism against such hostile assaults. Data mining and machine learning technologies have been extensively employed in network intrusion detection and prevention systems to extract user behaviour patterns from network traffic data. Association rules and sequence rules are the main foundations of data mining used for intrusion detection. Given the Auto encoder algorithm's traditional method's bottleneck of frequent itemsets mining, we provide a Length-Decreasing Support to Identify Intrusion based on Data Mining, which is an upgraded Data Mining Techniques based on Machine Learning for IDS. Based on test results, it appears that the suggested strategy is successful

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“…CART: Any classification or regression tree's study aims to establish a series of if-then scenarios that permit precise case prediction or categorization. Based on the collection of if-else conditions, classification as well as regression trees offer precise predictions [28][29][30]. CART makes it possible to quickly classify fresh observations.…”

Section: Classification Techniquesmentioning

confidence: 99%

Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy

Venkatarathinam,

Sivakami,

Christodoss

et al. 2023

IJRITCC

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Chronic kidney disease (CKD) affects millions of people worldwide, greatly reducing their quality of life and creating serious economic, social, and medical problems. Some automated diagnosis methods can detect chronic renal disease. In-depth studies on data mining techniques have recently focused on accuracy in the diagnosis of chronic renal illnesses, either by taking advantage of the disease's simplicity or doing feature selection in addition to pre-processing. In order to handle the unbalanced dataset in this work, Synthetic Minority Over Sampling Technique (SMOTE) is used during pre-processing. For this investigation, 400 data from the publicly accessible UCI machine learning (ML) repository are used. For the implementation, both homogeneous and heterogeneous ensemble classifiers which combine two separate classifiers have been used. Different machine learning (ML) techniques, such as the Classification and Regression Tree (CART), Adaboost classifier, Decision Tree (DT), Reduced Error Pruning Tree, Alternating Decision Tree, and Random Forests Algorithm and their ensembles with a significant reduction in entropy, are used to perform the classification. With a 99.12% accuracy rate and a 99.10% f1 score, the homogeneous classifier Adaboost-Random Forest outperforms other models in the prediction of CKD.

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Forecasting of Heart Diseases in Early Stages Using Machine Learning Approaches

Cited by 11 publications

References 6 publications

Early Detection of Cancer using Machine Learning (ML) Techniques

Early Detection of Cancer using Machine Learning (ML) Techniques

Implementation of Machine Learning-Based Data Mining Techniques for IDS

Ensemble of Homogenous and Heterogeneous Classifiers using K-Fold Cross Validation with Reduced Entropy

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