1. A computer-implemented system for early identification of depression risk among college students, the system comprising: - a data collection module configured to collect multi-dimensional student-related data including academic performance, attendance patterns, behavioural changes, lifestyle indicators, stress-related indicators, and social interaction metrics; - a data preprocessing module configured to perform cleaning, normalization, temporal alignment, and privacy filtering of the collected data; - a feature integration module configured to combine the processed data into a unified multi-dimensional representant; - an artificial intelligence-based analysis engine configured to analyse patterns, relationships, and temporal changes in the integrated data; - a factor mapping module configured to determine relative influence of multiple contributing factors and generate a factor influence representation; - a risk assessment module configured to compute a composite risk score and classify the score into predefined categories; and an output interface configured to present risk indication along with contributing factors for decision support.

2. The system as claimed in claim 1, wherein the data preprocessing module is further configured to remove incomplete or noisy data and standardize data values for consistent analysis.

3. The system as claimed in claim 1, wherein the feature integration module is configured to transform multiple categories of student-related data into measurable indicators and preserve relationships among contributing factors.

4. The system as claimed in claim 1, wherein the artificial intelligence-based analysis engine is configured to identify deviations in behavioural and activity patterns over time based on historical and ongoing data.

5. The system as claimed in claim 1, wherein the factor mapping module is configured to assign influence weights to contributing factors and identify dominant factors affecting the computed risk.

6. The system as claimed in claim 1, wherein the risk assessment module is configured to classify the computed risk score into categories including low risk, moderate risk, and high risk. The output interface is configured to provide interpretable results including contributing factors and trend information over time.

7. The system as claimed in claim 1, wherein the system is configured to continuously monitor changes in student-related data and generate early warning indications based on detected patterns.

8. A computer-implemented method for early identification of depression risk among college students, the method comprising; - collecting multi-dimensional student-related data from multiple sources; - preprocessing the data by performing cleaning, normalization, temporal alignment, and privacy filtering; - integrating the data into a unified multi-dimensional representation; analysing patterns and temporal changes using an artificial intelligence-based model; - determining influence of contributing factors using factor mapping; computing a composite risk score; - and classifying the risk into predefined categories for early warning and decision support.

9. The method as claimed in claim 10, further comprising continuously monitoring data over time to detect gradual changes in behavioural and activity patterns.

10. The method as claimed in claim 10, further comprising presenting risk levels along with contributing factors in an interpretable format for decision-making.

Description:FIELD OF THE INVENTION
[001] The present invention relates broadly to healthcare technology and data-driven monitoring systems, and more particularly to a computer-implemented system and method for early identification of depression risk among college students using artificial intelligence, multi-factor data processing, and dynamic factor mapping. The invention is configured to analyze student-related activity and behavioral patterns to generate risk indicators and support preventive decision-making in academic and institutional environments.
BACKGROUND FOR THE INVENTION:
[002] In educational environments, particularly among college students, increasing academic pressure, social expectations, financial stress, and lifestyle changes contribute significantly to mental well-being challenges. In many cases, early signs of depression remain unnoticed due to the lack of effective monitoring systems and the reluctance of students to openly express their emotional state. This often results in delayed identification and intervention, thereby increasing the severity of mental health issues over time.
[003] Existing techniques for identifying mental health concerns primarily rely on self-reported questionnaires, periodic counselling sessions, or manual observation by faculty members. These approaches are often subjective, reactive, and limited in scalability. Additionally, many available digital tools focus on single data sources such as mood tracking or survey responses, which provide only partial insights and fail to capture the combined influence of multiple factors affecting student well-being.
[004] Despite advancements in digital mental health solutions, several limitations persist in current methods. Existing systems often lack the capability to analyze multiple student-related factors in an integrated manner and do not effectively capture changes over time. Furthermore, they fail to provide clear explanations regarding the contributing factors behind identified risks, making it difficult for institutions to take targeted and timely action. The absence of continuous monitoring and comprehensive analysis limits the effectiveness of such systems in early identification of depression risk.
OBJECTS OF THE INVENTION:
[005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
[006] Object of the invention is to provide a computer-implemented system for early identification of depression risk among college students by analysing multiple student-related factors including academic performance, attendance patterns, sleep behaviour, stress indicators, and social interaction.
[007] Another object of the invention is to provide a multi-factor data processing approach that integrates academic, behavioural, lifestyle, and social indicators into a unified representation to enable comprehensive assessment of student well-being.
[008] Another object of the invention is to provide an artificial intelligence-based analysis mechanism configured to identify patterns, relationships, and changes in student-related data over time for detecting early warning signs associated with depression risk.
[009] Another object of the invention is to provide a dynamic factor mapping mechanism configured to determine the relative influence of multiple contributing factors and to highlight dominant factors affecting the identified risk.
[010] A further object of the invention is to provide a risk assessment module configured to generate a composite risk score and classify the risk into predefined categories including low, moderate, and high risk.
[011] Another object of the invention is to provide an interpretable output mechanism that presents risk levels along with contributing factors to support decision-making by authorized personnel.
[012] Another object of the invention is to provide a continuous monitoring and early warning system that enables timely preventive intervention while functioning as a non-diagnostic support tool.
SUMMARY OF THE INVENTION:
[013] The present invention is described in the following sections by various embodiments. However, it should be understood that the invention can be implemented in various forms and is not limited to the specific embodiment provided herein. In the context of the present disclosure, it should be understood that the described embodiments in this section are put forth for illustrative purposes only. Those skilled in the art will appreciate that various modifications, adaptations, and alternative designs may be employed without departing from the scope and spirit of the invention. Accordingly, the present invention should not be limited to the specific embodiments illustrated herein, but rather should be construed according to the claims and description that follow.
[014] Embodiments of the present invention provide a computer-implemented system for early identification of depression risk among college students, comprising a data collection module configured to collect multi-dimensional student-related data including academic indicators, attendance patterns, behavioural changes, lifestyle indicators such as sleep patterns, stress-related indicators, and social interaction metrics; a data preprocessing module configured to clean, normalize, temporally organize, and filter the collected data; a feature integration module configured to combine the processed data into a unified multi-dimensional representation; an artificial intelligence-based analysis engine configured to analyse patterns, relationships, and temporal changes in the integrated data; a factor mapping module configured to determine relative influence of contributing factors and generate a factor influence representation; a risk assessment module configured to compute a composite risk score and classify the score into predefined categories; and an output interface configured to provide risk indication along with contributing factors for decision support. The technical advantage of the system lies in its ability to identify early warning signs of depression risk through multi-factor analysis and provide interpretable outputs for timely intervention. The system further provides interpretable outputs explaining the contribution of individual factors influencing the computed risk score. The invention provides a technical effect in terms of improved multi-dimensional data processing, enhanced pattern detection, and efficient computation through structured factor mapping.
[015] In accordance with an embodiment of the present invention, the data preprocessing module is further configured to perform data cleaning, normalization, temporal alignment, and privacy filtering to ensure consistency and security of the data. In accordance with an embodiment of the present invention, the feature integration module is configured to transform multiple categories of student-related data into measurable indicators and combine them into a unified feature set while preserving relationships among the factors. In accordance with an embodiment of the present invention, the artificial intelligence-based analysis engine is configured to identify patterns, detect deviations from normal behaviour, and continuously learn from historical and ongoing data to estimate depression risk levels.
[016] In accordance with an embodiment of the present invention, the factor mapping module is configured to assign influence weights to different contributing factors and determine their impact on the overall risk, thereby highlighting dominant factors affecting student mental well-being. In accordance with an embodiment of the present invention, the risk assessment module is configured to aggregate weighted factors, compute a risk score, and classify the risk into categories including low, moderate, and high risk. In accordance with an embodiment of the present invention, the output interface is configured to present risk levels, factor contribution summaries, and trend information in an interpretable format to support decision-making by authorized users.
[017] In another embodiment of the present invention, a computer-implemented method for identifying depression risk among college students comprises collecting multi-dimensional student-related data from multiple sources; preprocessing the data through cleaning, normalization, temporal alignment, and privacy filtering; integrating the data into a unified representation; analysing relationships and temporal changes using an artificial intelligence-based model; determining influence weights of contributing factors through factor mapping; generating a composite risk score based on combined influence of the factors; and classifying the risk into predefined categories for early warning and decision support. The method further includes continuous monitoring of temporal variations in student-related data to identify gradual behavioural changes over time.
[018] In accordance with an embodiment of the present invention, the method further comprises continuously monitoring changes in student-related data over time to detect gradual variations in behaviour and activity patterns, and generating alerts or risk indications when combined changes exceed predefined thresholds, thereby enabling timely preventive intervention while functioning as a non-diagnostic support system.
BRIEF DESCRIPTION OF DRAWINGS:
[019] In order to facilitate a comprehensive understanding of the detailed features of the present invention, a more specific description of the invention, briefly summarized above, may have been referenced through various embodiments, some of which are depicted in the accompanying drawings. It should be emphasized, however, that the provided drawings merely exemplify typical embodiments of the present invention and should not be construed as limiting its scope, as the invention may encompass other equally efficacious embodiments.
[020] These and additional features, advantages, and benefits of the present invention will become apparent by consulting the following textual illustration, wherein similar reference numerals denote similar components throughout the various views.
[021] Fig. 1 illustrates the overall system architecture of the proposed invention;
[022] Fig. 2 illustrates the Data Pre-Processing Flow;
[023] Fig. 3 illustrates the Feature Integration model;
[024] Fig. 4 illustrates the Factor Mapping and Influence Analysis;
[025] Fig. 5 illustrates the Risk Scoring and Classification Flow;
[026] Fig. 6 illustrates the visualization and decision support interface.
DETAILED DESCRIPTION OF INVENTION:
[027] The present invention is subsequently described herein using various embodiments with reference to the accompanying drawing, wherein the reference numerals utilized in the accompanying drawing correspond to the similar elements throughout the description. While the present invention is illustratively described herein by way of example using embodiments and accompanying drawings, those skilled in the art will acknowledge that the invention is not limited to the described embodiments or drawings and is not intended to represent the scale of the different components. Furthermore, certain components that may constitute a part of the invention might not be depicted in specific figures for the purpose of simplified illustration, and such omissions do not restrict the outlined embodiments in any manner. It should be comprehended that the drawings and the detailed description provided are not intended to limit the invention to the particular disclosed form, but instead, the invention is intended to encompass all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim. Throughout this description, the term 'may' is used in a permissive sense, indicating the potential to, rather than in a mandatory sense, indicating a requirement. Additionally, the words 'a' or 'an' signify at least one, and the word 'plurality' signifies 'one or more' unless otherwise specified. Moreover, the terminology and phraseology employed herein are solely for descriptive purposes and should not be construed as limiting in scope. Terms such as 'including', 'comprising', 'having', 'containing', or 'involving', and their variations, are intended to be broad and encompass the listed subject matter thereafter, as well as equivalents and additional subject matter not explicitly mentioned, and should not be interpreted as excluding other additives, components, integers, or steps. Similarly, the term 'comprising' is considered synonymous with the terms 'including' or 'containing' for applicable legal purposes.
[028] The invention relates to a computer-implemented system and method for early identification of depression risk among college students. The disclosed system comprises a data collection module configured to gather multi-dimensional student-related data including academic performance, attendance patterns, behavioural changes, lifestyle indicators such as sleep patterns, stress-related indicators, and social interaction metrics. The collected data is further processed using a data preprocessing module configured to perform cleaning, normalization, temporal organization, and privacy filtering.
[029] The system further comprises a feature integration module configured to convert the processed data into measurable indicators and integrate them into a unified multi-dimensional feature set. The integrated representation preserves relationships among various contributing factors, enabling comprehensive analysis of student-related patterns.
[030] The system includes an artificial intelligence-based analysis engine configured to analyse patterns, relationships, and temporal changes in the integrated data. The analysis engine identifies deviations from normal behaviour and estimates depression risk levels based on combined influence of multiple factors.
[031] A factor mapping module is provided as a core component of the invention, configured to assign relative influence weights to individual contributing factors and determine their impact on the overall risk. The factor mapping module further generates a representation highlighting dominant contributing factors affecting the identified risk.
[032] The system further comprises a risk assessment module configured to aggregate weighted factor values and compute a composite risk score. The computed score is classified into predefined categories including low risk, moderate risk, and high risk.
[033] An output interface is provided to present the risk indication along with contributing factors and trend information in an interpretable format. The output enables authorized users such as counsellors or institutional staff to take timely and informed decisions.
[034] In one embodiment of the invention, the data collection module gathers information from multiple sources including academic records, behavioural observations, lifestyle indicators, and social interaction patterns, thereby capturing multiple weak signals which collectively provide a stronger indication of student well-being.
[035] In another embodiment, the data preprocessing module performs data cleaning to remove incomplete or noisy entries, normalization to standardize data ranges, temporal alignment to organize data into time-based sequences, and privacy filtering to remove or mask sensitive information.
[036] In accordance with an embodiment of the invention, the feature integration module combines different categories of data into a unified representation, enabling the system to analyse how multiple factors interact rather than treating them independently.
[037] In another embodiment, the artificial intelligence-based analysis engine continuously learns from historical and ongoing data to improve pattern recognition and adapt to changing student behaviour over time.
[038] In accordance with an embodiment, the factor mapping module assigns varying levels of influence to different factors such as academic stress, sleep irregularity, and social interaction changes, thereby identifying dominant contributors to the computed risk.
[039] In one embodiment, the risk assessment module computes the overall risk score by aggregating weighted factors and applies classification thresholds to categorize the risk into low, moderate, or high levels.
[040] The output interface may further display trend information indicating progression of risk over time and provide alerts when significant changes are detected.
[041] Figure 1 illustrates the overall system architecture of the present invention. The system comprises a data collection module, a data preprocessing module, a feature integration module, an artificial intelligence-based analysis engine, a factor mapping module, a risk assessment module, and an output interface. The data collection module gathers multi-dimensional student-related data including academic indicators, attendance patterns, behavioural changes, lifestyle indicators, stress-related indicators, and social interaction metrics. The collected data is transmitted to the preprocessing module and further processed through subsequent modules for analysis and risk identification.
[042] Figure 2 illustrates the data preprocessing workflow of the system. The preprocessing module performs operations including data cleaning to remove incomplete or noisy data, normalization to standardize data values, temporal alignment to organize data into time-based sequences, and privacy filtering to remove or mask sensitive information. The processed data is then forwarded to the feature integration module for further analysis
[043] Figure 3 illustrates the feature integration process of the invention. In this stage, multiple categories of processed data including academic, behavioural, lifestyle, stress-related, and social interaction data are converted into measurable indicators and combined into a unified multi-dimensional feature set. The integration preserves relationships among different factors, enabling the system to analyse their combined influence on student well-being.
[044] Figure 4 illustrates the factor mapping and influence analysis mechanism. The factor mapping module assigns influence weights to different contributing factors and determines their relative impact on the overall depression risk. The module further identifies dominant contributing factors and generates a representation indicating how each factor contributes to the computed risk level.
[045] Figure 5 illustrates the risk assessment and classification process. The risk assessment module aggregates weighted factor values obtained from the factor mapping module to compute a composite risk score. The computed score is then classified into predefined categories such as low risk, moderate risk, and high risk based on defined thresholds.
[046] Figure 6 illustrates the visualization and decision support interface of the system. The interface displays the identified risk level along with contributing factors and trend information over time. The output is presented in an interpretable format to assist authorized users in understanding the risk and taking timely preventive actions.
[047] Various modifications to these embodiments are evident to those skilled in the art based on the description and accompanying drawings. The principles associated with the various embodiments described herein can be applied to additional embodiments. Consequently, the description is not intended to be limited to the embodiments shown in conjunction with the accompanying drawings but aims to provide the broadest scope consistent with the principles and the innovative and inventive features disclosed or suggested herein. Therefore, the invention is expected to encompass all other such alternatives, modifications, and variations falling within the scope of the present invention and the appended claims. , Claims:We Claim:
1. A computer-implemented system for early identification of depression risk among college students, the system comprising:
- a data collection module configured to collect multi-dimensional student-related data including academic performance, attendance patterns, behavioural changes, lifestyle indicators, stress-related indicators, and social interaction metrics;
- a data preprocessing module configured to perform cleaning, normalization, temporal alignment, and privacy filtering of the collected data;
- a feature integration module configured to combine the processed data into a unified multi-dimensional representant;
- an artificial intelligence-based analysis engine configured to analyse patterns, relationships, and temporal changes in the integrated data;
- a factor mapping module configured to determine relative influence of multiple contributing factors and generate a factor influence representation;
- a risk assessment module configured to compute a composite risk score and classify the score into predefined categories; and an output interface configured to present risk indication along with contributing factors for decision support.
2. The system as claimed in claim 1, wherein the data preprocessing module is further configured to remove incomplete or noisy data and standardize data values for consistent analysis.
3. The system as claimed in claim 1, wherein the feature integration module is configured to transform multiple categories of student-related data into measurable indicators and preserve relationships among contributing factors.
4. The system as claimed in claim 1, wherein the artificial intelligence-based analysis engine is configured to identify deviations in behavioural and activity patterns over time based on historical and ongoing data.
5. The system as claimed in claim 1, wherein the factor mapping module is configured to assign influence weights to contributing factors and identify dominant factors affecting the computed risk.
6. The system as claimed in claim 1, wherein the risk assessment module is configured to classify the computed risk score into categories including low risk, moderate risk, and high risk. The output interface is configured to provide interpretable results including contributing factors and trend information over time.
7. The system as claimed in claim 1, wherein the system is configured to continuously monitor changes in student-related data and generate early warning indications based on detected patterns.
8. A computer-implemented method for early identification of depression risk among college students, the method comprising;
- collecting multi-dimensional student-related data from multiple sources;
- preprocessing the data by performing cleaning, normalization, temporal alignment, and privacy filtering;
- integrating the data into a unified multi-dimensional representation;
analysing patterns and temporal changes using an artificial intelligence-based model;
- determining influence of contributing factors using factor mapping;
computing a composite risk score;
- and classifying the risk into predefined categories for early warning and decision support.
9. The method as claimed in claim 10, further comprising continuously monitoring data over time to detect gradual changes in behavioural and activity patterns.
10. The method as claimed in claim 10, further comprising presenting risk levels along with contributing factors in an interpretable format for decision-making.