AI Tool for Early-Stage Dementia Detection

Abstract

The fact that dementia causes damage to brain functions and even communication in people cannot be overemphasized. However, the difficulty lies in the fact that the disorder occurs in a very covert manner because the symptoms are gradual and hence difficult to detect at an early stage of the disease. There are no specific tests for diagnosing dementia. In this context, this paper aims to propose a web-based AI-powered tool for early detection of dementia, replacing conventional methods of dementia detection by providing a comprehensive tool for speech analysis and cognitive tests, making it easier and convenient for early detection of dementia. The tool will have the ability to gather data from the user, which will enable the comprehensive analysis of the user’s cognitive state by extracting features related to the user’s speech. The data collected would be passed through a number of algorithms. This would help in extracting features and classifying the data by making use of the Random Forest, SVM, and Logistic Regression algorithms. The ensemble method will be used to ensure accurate predictions. The tool will have the ability to generate a risk score related to dementia. This will enable the user to be classified according to their risk level. This will ensure that the results are clear to the user, thus enabling them to make the right decisions. The proposed tool will be able to provide a cost-effective, convenient, and non-intrusive tool for early detection of dementia, making it easier for people to consult their doctors, thus improving their health status.

Introduction

The text discusses the importance of early dementia detection and highlights how dementia gradually affects memory, communication, thinking, and behavior. Early diagnosis is challenging because initial symptoms, such as memory loss and language difficulties, are often mistaken for normal aging. Recent research shows that speech, language patterns, and cognitive test results can serve as effective non-invasive indicators of dementia, as they reflect underlying brain activity.

Advancements in artificial intelligence and machine learning have enabled the processing and classification of speech and cognitive data using techniques such as speech recognition, natural language processing, deep learning, and multimodal systems. Existing studies have explored dementia detection using speech features like pitch, pauses, fluency, and linguistic patterns, as well as cognitive assessments. Some approaches also combine text, speech, facial analysis, and medical data such as MRI scans. Machine learning models including Random Forest, Support Vector Machine (SVM), Logistic Regression, XGBoost, LSTM, and deep embeddings like wav2vec have been applied for accurate classification and prediction.

Despite these developments, current systems face several limitations. Many approaches rely on only one type of input, such as speech or cognitive tests, which fails to capture the complete cognitive condition of an individual. Some systems depend heavily on expensive medical imaging and clinical data, making them unsuitable for large-scale public screening, especially in resource-limited regions. Additionally, many AI models remain research-oriented and are not implemented as practical user-friendly applications. Deep learning methods also require large datasets and high computational resources.

To address these gaps, the proposed system introduces a multimodal approach that combines both speech analysis and cognitive test data for early dementia detection. The workflow includes data collection, preprocessing, feature extraction, and classification using machine learning algorithms such as Random Forest, SVM, and Logistic Regression. Speech-related features like pitch, pauses, and speech rate are extracted, while cognitive test results are converted into numerical representations. An ensemble learning approach is further used to improve the accuracy and reliability of the system.

Overall, the proposed framework aims to provide a more comprehensive, accessible, and accurate method for early dementia screening by integrating speech and cognitive analysis through AI and machine learning techniques.

Conclusion

Therefore, it will be reasonable to claim that the described solution could serve as an excellent alternative for diagnosing dementia via the use of machine learning technologies and voice recognition and cognitive testing. At the same time, the presented method will be able to provide risk assessment among its users. The use of the online approach will help prevent expenses related to buying new technical facilities. However, it would be necessary to highlight that this approach will not be utilized as a diagnostic method but rather be used as a means for identifying early symptoms. Some potential methods for further improvement may include the use of deep learning algorithms along with multiple language options.

References

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Copyright

Copyright © 2026 Dr. G. Saritha, M. Manish, Gopalapuram Vishnu Priya, Muhammad Ahmed Ali Zulfi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.