MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541134070 A) filed by Karpagam Academy Of Higher Education; Karpagam Institute Of Technology; Renuga K; and Mohammed Aashim K, Coimbatore, Tamil Nadu, on Dec. 31, 2025, for 'healthcare data analysis for disease prediction using python.'
Inventor(s) include Renuga K; and Mohammed Aashim K.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "Healthcare data analysis has become an essential component in modern clinical decision-making, enabling early disease detection and improving patient outcomes through data-driven insights. This study presents a Python-based machine learning framework designed for accurate disease prediction using structured healthcare datasets. The proposed system incorporates a comprehensive workflow consisting of data preprocessing, feature engineering, model training, and performance evaluation. Data preprocessing techniques-including normalization, missing-value imputation, outlier detection, and categorical encoding-ensure the integrity and usability of heterogeneous medical records. Feature engineering and selection methods are employed to identify relevant clinical attributes that contribute significantly to disease classification, thereby enhancing both model interpretability and predictive accuracy. The framework integrates multiple supervised learning algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Artificial Neural Networks to analyze patient data and generate disease predictions. These models are trained and evaluated using well-established metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, ensuring robust performance across different disease categories. Comparative analysis enables identification of the most suitable algorithm for deployment based on dataset characteristics and diagnostic requirements. Additionally, the system incorporates data visualization and statistical analysis modules to support exploratory data assessment through correlation matrices, distribution plots, and trend analysis. These visual insights aid clinicians and researchers in understanding underlying patterns within patient populations and identifying potential risk factors. The proposed solution is implemented using Python and its scientific computing libraries, including Pandas, NumPy, Scikit-learn, Matplotlib, and TensorFlow, providing high extensibility and computational efficiency. By integrating automation, intelligent modeling, and analytical visualization, this framework offers a scalable and reliable approach for early disease prediction in healthcare environments. The system has the potential to enhance clinical decision support, reduce diagnostic errors, and promote proactive healthcare management, thereby contributing to improved patient care and reduced healthcare costs."
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