Geetha Narasimhan

EDUCATION

Pursuing Ph.D
Mtech in computer science and Engineering,
MCA
B.Sc

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Multidisciplinary, Cardiology and Cardiovascular Medicine, Human-Computer Interaction

Scopus Publications

A hybrid approach with metaheuristic optimization and random forest in improving heart disease prediction
Geetha Narasimhan, Akila Victor
Scientific Reports, 2025
Cardiovascular diseases (CVD) a major cause of morbidity and mortality among the world’s non-communicable disease incidences. Though these practices are in use for diagnostics of different CVDs in clinical settings, need improvement because they are solving the purpose of only 57% of the patients in emergency. Due to this cost of diagnosis for heart disease is increasing which is the reason for analyzing heart disease and predicting it as early as possible. The main motive of this paper is to find an intelligent method for predicting disease effectively by means of machine learning (ML) and metaheuristic algorithms. Optimization techniques have the merit of handling non-linear complex problems. In this paper, an efficient ML model along with metaheuristic optimization techniques is evaluated for heart disease dataset to enhance the accuracy in predicting the disease. This will help to reduce the death rate due to the severity of heart disease. The SelectKBest feature selection is applied to the Cleveland Heart dataset and overall rank is obtained. Accuracy is measured. The optimization techniques namely Genetic Algorithm Optimized Random Forest (GAORF), Particle Swarm Optimized Random Forest (PSORF), and Ant Colony Optimized Random Forest (ACORF) are applied to the Cleveland dataset. Classification algorithms are performed before and after optimization. The output of the experiment explains that the GAORF performed better for the dataset considered. Also, a comparison is made along with the SelectKBest filter methods. The proposed model achieved better accuracy which is the maximum among other optimization and classification techniques.
Empirical analysis of predicting heart disease using diverse datasets and classification procedures of machine learning
Geetha Narasimhan, Akila Victor
Ain Shams Engineering Journal, 2025
Cardiovascular disease (CVD) poses a significant threat due to its complexity and fatality, necessitating early intervention. Fortunately, the rapidly evolving field of machine learning (ML) offers an array of algorithms for disease diagnosis and prediction. This research aims to develop and identify a model that assists radiologists in predicting heart disease, a two-phased approach. Phase 1: Feature Selection with SelectKBest: The first phase utilizes the SelectKBest method to select the most relevant features for prediction. This method combines individual feature rankings based on three statistical tests: chi-squared, mutual information, and F-statistic. The final selection is based on the overall rank obtained by each feature. Phase 2: Classification Algorithm Exploration: The second phase applies various classification algorithms, including Random Forest, k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest Grid search, Gradient Boost, and Neural Network. The performance of these models is evaluated using three standard heart disease datasets: Cleveland, Faisalabad, and Framingham, retrieved from UCI and Kaggle. Each dataset undergoes pre-processing before applying the SelectKBest feature selection and all ML algorithms. Across all three datasets, Random Forest emerged as the champion, achieving accuracy rates of 90.16%, 90%, and 84%, respectively. Additionally, it demonstrated consistently lower classification errors compared to other algorithms. This research highlights the effectiveness of feature selection, particularly the SelectKBest filter-based method, in improving heart disease prediction accuracy using machine learning models like Random Forest. This paves the way for integrating such models into clinical settings, empowering radiologists with valuable decision-making tools for early CVD detection and intervention.
Bio-inspired disease prediction: harnessing the power of electric eel foraging optimization algorithm with machine learning for heart disease prediction
Geetha Narasimhan, Akila Victor
Artificial Intelligence Review, 2024
Heart disease is the most significant health problem around the world. Thus, it emphasizes the need for accurate and efficient predictive models for early diagnosis. This study proposes an innovative approach integrating the Electric Eel Foraging Optimization Algorithm (EEFOA) with the Random Forest (RF) algorithm for classifying heart disease prediction. EEFOA draws inspiration from the foraging behaviour of electric eels, a bio-inspired optimization framework capable of effectively exploring complex solutions. The objective is to improve the predictive performance of heart disease diagnosis by integrating optimization and Machine learning methodologies. The experiment uses a heart disease dataset comprising clinical and demographic features of at-risk individuals. Subsequently, EEFOA was applied to optimize the features of the dataset and classification using the RF algorithm, thereby enhancing its predictive performance. The results demonstrate that the Electric Eel Foraging Optimization Algorithm Random Forest (EEFOARF) model outperforms traditional RF and other state-of-the-art classifiers in terms of predictive accuracy, sensitivity, specificity, precision, and Log_Loss, achieving remarkable scores of 96.59%, 95.15%, 98.04%, 98%, and 0.1179, respectively. The proposed methodology has the potential to make a significant contribution, thereby reducing morbidity and mortality rates.
Blockchain in the healthcare domain and performing various security analysis
Suresh Kumar Nagarajan, Geetha Narasimhan, Akila Victor, Yash Vaish, Pranshu Tripathi
Blockchain and Iot Based Smart Healthcare Systems, 2024
Blockchain is a promising technology that can be used to improve the healthcare system. It can be used to store patient data securely and prevent tampering. It can also be used to improve supply chain management by increasing transparency and interoperability. This work proposes a web-based application that uses blockchain to store patient’s data and retailer’s information. The application will also be able to send encrypted messages securely and anonymously. The application will be deployed on the Ethereum platform. The benefits of using blockchain in healthcare are Security: Blockchain is a secure way to store data because it is decentralized and encrypted. This makes it difficult for unauthorized users to access or tamper with data. Transparency: Blockchain is transparent, which means that all transactions are recorded on the blockchain and can be viewed by anyone. This can help to increase trust and accountability in the healthcare system. Interoperability: Blockchain can be used to connect different healthcare systems together, which can improve the flow of information. This can help to improve patient care. Immutability: Blockchain is immutable, which means that data cannot be changed once it is added to the blockchain. This can help to ensure the accuracy of data. The challenges of using blockchain in healthcare are Complexity, Cost, and Regulation. Despite these challenges, blockchain is a promising technology that has the potential to improve the healthcare system. This work is a step towards realizing the potential of blockchain in healthcare.
Grey wolf optimized stacked ensemble machine learning based model for enhanced efficiency and reliability of predicting early heart disease
Geetha Narasimhan, Akila Victor
Automatika, 2024
Heart disease is one of the foremost reasons for death globally.Machine learning (ML) can be used to predict heart diseases early, which can help improve patient outcomes.This research proposes a novel machine learning method for predicting heart disease using a combination of Grey Wolf Optimization (GWO) and stacked ensemble techniques.GWO is a metaheuristic algorithm that can be used to optimize the parameters of machine-learning models.The stacked ensemble technique is a combination of multiple machine learning models to improve the overall accuracy of the prediction.The model proposed was evaluated using a dataset of heart patients.The results showed that the model achieved a 93% accuracy, which was significantly higher compared to traditional machine learning methods.The proposed method also had a higher precision of 91%, sensitivity of 95.3%, F1 score of 92.9%, and Matthew coefficient of 0.83, less in Log_Loss 2.87 than the traditional methods.The results of this research suggest that the proposed model is a promising new approach for predicting heart diseases.This method is more accurate and reliable than traditional methods and has the potential to improve patient outcomes.
Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines
Akila Victor, Geetha Narasimhan
Journal of Education and Health Promotion, 2023
The word disease is a common word and there are many diseases like heart disease, diabetes, breast cancer, COVID-19, and kidney disease that threaten humans. Data-mining methods are proving to be increasingly beneficial in the present day, especially in the field of medical applications; through the use of machine-learning methods, that are used to extract valuable information from healthcare data, which can then be used to predict and treat diseases early, reducing the risk of human life. Machine-learning techniques are useful especially in the field of health care in extracting information from healthcare data. These data are very much helpful in predicting the disease early and treating the patients to reduce the risk of human life. For classification and decision-making, data mining is very much suitable. In this paper, a comprehensive study on several diseases and diverse machine-learning approaches that are functional to predict those diseases and also the different datasets used in prediction and making decisions are discussed in detail. The drawbacks of the models from various research papers have been observed and reveal countless computational intelligence approaches. Naïve Bayes, logistic regression (LR), SVM, and random forest are able to produce the best accuracy. With further optimization algorithms like genetic algorithm, particle swarm optimization, and ant colony optimization combined with machine learning, better performance can be achieved in terms of accuracy, specificity, precision, recall, and specificity.
Long short-term memory-based neural networks in an AI music generation platform
Suresh Kumar Nagarajan, Geetha Narasimhan, Ankit Mishra, Rishabh Kumar
Deep Learning Research Applications for Natural Language Processing, 2022
Music is an essential component of a promotional video since it helps to establish a brand's or entity's identity. Music composition and production, on the other hand, is quite costly. The expense of engaging a competent team capable of creating distinctive music for your firm could be prohibitively expensive. In the last decade, artificial intelligence has accomplished feats previously unimaginable to humanity. Artificial intelligence can be a lifesaver, not only in terms of the amount of money a company would have to spend on creating their own unique music but also in terms of the amount of time and work required on the firm's part. A web-based platform that can be accessed from anywhere in the world would help the product obtain customers without regard to geography. AI algorithms can be taught to recognize which sound combinations produce a pleasing melody (or music). Multiple machine learning algorithms can be used to accomplish this.
MorseEx: A communication application for the deaf-blind
Suresh Kumar Nagarajan, Geetha N., Raghav Talwar, Shivoma Ahuja
Deep Learning Research Applications for Natural Language Processing, 2022
MorseEx uses Morse code, which allows partially visually impaired and hard of hearing people to chat with others. In Morse code, letters are represented as a combination of dots and dashes. The person inputs a dot by tapping on the left of the screen, dash by tapping on the center of the screen to form a message, and tapping on the right will separate letters of the message, and tapping it twice sends the message. This message will be saved to the database and then converted to a normal text message to receive by people who do not have any impairments. On the other hand, people with no impairments have to type and send text messages. For this message to be understood by the visually and partially impaired, a dot will be produced as short vibration and a dash will be produced as long vibration. The model will be developing an Android mobile application using Android studio and Firebase database to store user information. The aim is to contribute to society in any way possible.

Publications

Geetha Narasimhan & Akila Victor (2024) Grey wolf optimized stacked ensemble machine learning based model for enhanced efficiency and reliability of predicting early heart disease, Automatika, 65:3, 749-762, DOI: 10.1080/00051144.2024.2317098