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All Journal International Journal of Public Health Science (IJPHS)
Shankar, Reddy Shiva
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Health Professions

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Validation on selected breast cancer drugs of physicochemical features by using machine learning models Gupta, Vuddagiri MNSSVKR; Krishna, Chitta Venkata Phani; Murthy, Konakanchi Venkata Subrahmanya Srirama; Shankar, Reddy Shiva
International Journal of Public Health Science (IJPHS) Vol 13, No 2: June 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i2.23322

Abstract

Breast cancer is one of the leading causes of death among females today. The elbow approach determines the ideal number of clusters after determining that the Dataset is highly cluster able with the Hopkins statistic. Three distinct groups with distinct differences were produced using the dataset's proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM). Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE), Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson and Kessel - like fuzzy k-means with entropy regularization (FKM.GK.ENT), Gustafson and Kessel - like fuzzy kmeans with entropy regularization and noise (FKM.GK.ENT.NOISE), and PEMFKM, are evaluated. The partition coefficient (PC), partition entropy (PE), and Modified partition coefficient index (MPC) index values are better for FKM.GK than the suggested PEMFKM method. When compared to the FKM.GK method, the index values for the proposed PEMFKM algorithm have superior results for the parameters Silhouette (SIL), Xie and Beni index (XB), and fuzzy silhouette index (SIL.F). The results shows that the PEMFKM algorithm will provide better clusters and that the drugs in a given cluster may be combined for use in combination therapy for breast cancer treatment.
Comparative analysis of deep learning models for various nonalcoholic fatty liver disease datasets Srirama Murthy, Konakanchi Venkata Subrahmanya; Shankar, Reddy Shiva; Pradhan, Samarendra Narayana; Mohanty, Bhabodeepika; Rao, Veeranki Venkata Rama Maheswara
International Journal of Public Health Science (IJPHS) Vol 13, No 4: December 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i4.23891

Abstract

Fatty liver disease is caused by increased liver buildup or weight above 5-10%. This disorder is widespread in people with diabetes, overweight persons, and metabolic syndrome patients. Clinical decision support systems can improve liver failure diagnosis and prediction to reduce this situation. Many liver failure models have drawbacks, and liver failure prediction is still a problem. This work uses four large open-access critical care patient datasets to create and verify liver failure risk prediction models. This study aims to construct a clinically applicable diagnostic and predictive model that evaluates the probability or risk of liver failure in intensive care unit (ICU) patients using extreme gradient boosting (XGBoost), artificial neural networks (ANN), multi-layer perceptron (MLP), Modular Neural Network (MNN), and generalized feed forward (GFF). We evaluated performance metrics using these models: accuracy, sensitivity, specificity, and predictive accuracy.
Evaluation of stress based on multiple distinct modalities using machine learning techniques Pradhan, Samarendra Narayana; Shankar, Reddy Shiva; Barik, Shekharesh; Mohanty, Bhabodeepika; Rao, Venkata Rama Maheswara
International Journal of Public Health Science (IJPHS) Vol 13, No 2: June 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i2.23577

Abstract

Nowadays, one of the most time-consuming and complex study subjects is predicting working professionals' stress levels. It is thus crucial to estimate active professionals' stress levels to aid their professional development. Several machine learning (ML) and deep learning (DL) methods have been created in earlier articles for this goal. But they also have drawbacks, such as increased design complexity, a high rate of misclassification, a high incidence of mistakes, and reduced efficiency. Considering these issues, the objective of this study is to make a prognosis about the stress levels experienced by working professionals by using a cutting-edge deep learning model known as the convolutional neural networks (CNN). In this paper, we offer a model that combines CNN-based classification with dataset preprocessing, feature extraction, and optimum feature selection using principal component analysis (PCA). When the raw data is preprocessed, duplicate characteristics are eliminated, and missing values are filled.
Co-Authors Barik, Shekharesh Gupta, Vuddagiri MNSSVKR Krishna, Chitta Venkata Phani Mohanty, Bhabodeepika Murthy, Konakanchi Venkata Subrahmanya Srirama Pradhan, Samarendra Narayana Rao, Veeranki Venkata Rama Maheswara Rao, Venkata Rama Maheswara Srirama Murthy, Konakanchi Venkata Subrahmanya